# Databricks Reviews **Vendor:** Databricks Inc. **Category:** [Big Data Processing And Distribution Systems](https://www.g2.com/categories/big-data-processing-and-distribution) **Average Rating:** 4.6/5.0 **Total Reviews:** 761 ## About Databricks Databricks is a unified data and AI platform that helps organizations build, govern and scale data pipelines, analytics, machine learning, AI applications and agents. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on Databricks to work with enterprise data and AI at scale. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Agent Bricks, Lakeflow, Lakehouse, Lakebase, Genie and Unity Catalog. Founded in 2013 by the original creators of Apache Spark™, Delta Lake, MLflow and Unity Catalog, Databricks is built on an open lakehouse architecture that brings data, analytics and AI together. The platform is used by data engineers, data scientists, analysts, developers, machine learning teams, AI teams and business users to collaborate across the full data and AI lifecycle. Key Databricks capabilities include: - Data engineering: Build, automate and manage reliable batch, streaming and real-time data pipelines. - Analytics and business intelligence: Run SQL analytics, create dashboards and enable business teams to explore data. - Data governance: Discover, secure and manage data and AI assets across teams, clouds and workloads. - Machine learning and AI: Develop models, build generative AI applications and create production-grade AI agents. - Data applications: Build and deploy data-driven applications using governed enterprise data. Available across AWS, Azure and Google Cloud, Databricks helps organizations work across clouds, reduce data silos and simplify collaboration across teams and tools. Customers use Databricks for use cases such as customer personalization, fraud detection, predictive maintenance, real-time analytics, cybersecurity, healthcare research, financial risk management, supply chain optimization and AI-powered decision-making. Databricks is used across industries including financial services, healthcare and life sciences, retail, manufacturing, energy and the public sector. Organizations use the platform to modernize data infrastructure, accelerate AI adoption and turn enterprise data into business value. ## Databricks Pros & Cons **What users like:** - Users appreciate the **advanced AI features and robust data security** of Databricks, enhancing their machine learning capabilities. (287 reviews) - Users find **Databricks easy to use** , especially for model hosting and serving with seamless integrations and real-time data management. (277 reviews) - Users value the **seamless integrations** of Databricks with various tools, enhancing data handling and collaboration significantly. (189 reviews) - Users value the **excellent collaboration** features of Databricks, enabling real-time teamwork among data engineers and analysts. (150 reviews) - Users love the **effective data management features** of Databricks, simplifying complex tasks with powerful tools and integrations. (149 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (148 reviews) - Analytics (137 reviews) - Machine Learning (136 reviews) - ML Integration (135 reviews) - Scalability (132 reviews) **What users dislike:** - Users experience a **steep learning curve** with Databricks, complicating adoption and understanding due to its complexity. (112 reviews) - Users find Databricks to be **expensive** , particularly when handling large datasets and uncertain pricing structures. (97 reviews) - Users face a **steep learning curve** with Databricks, making initial adoption challenging for teams. (96 reviews) - Users struggle with the **missing features** of Databricks, limiting customization and complicating the development process. (68 reviews) - Users find the **complexity** of Databricks challenging, particularly due to the steep learning curve and integration limitations. (64 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (61 reviews) - Performance Issues (56 reviews) - Poor UI Design (53 reviews) - Difficult Learning (51 reviews) - Complex Setup (45 reviews) ## Databricks Reviews ### 1. Databricks Simplifies Big Data Processing and Team Collaboration **Rating:** 4.5/5.0 stars **Reviewed by:** Praveen M. | Associate Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** May 07, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it simplifies large-scale data processing and collaboration in one platform. The integration with Spark and cloud service makes handling big data much more efficient. I also like the notebook environment, which makes it easier for teams for works together on analytics and machine learning tasks. **What do you dislike about Databricks?** One thing I dislike about Databricks is the platform can feel complex for new users, especially when managing clusters and configurations. Pricing can also become expensive with larger workloads if resources are not optimized carefully. While integrations and AI features are powerful, the onboarding process and support documentation could be more beginner-friendly. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of processing and analyzing large amounts of data efficiently in one platform. It combines data engineering, analytics and AI workflows, which reduce the need for the multiple separate tools. This improves collaboration, speeds up data processing, and helps generate insights much faster. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks helpful for simplifying large-scale data processing and collaboration. Our integration with Spark and cloud services is designed to make handling big data more efficient. ### 2. Powerful Lakehouse Platform for Scalable Pipelines and Collaboration **Rating:** 3.5/5.0 stars **Reviewed by:** Shweta D. | Enterprise Data Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** May 06, 2026 **What do you like best about Databricks?** in my role i focus on designing scalable and future ready data platform, and databricks has become a key part of that architecture i have used it across multiple project for building data pipelines, enabling analytics, and support data science teams. what stand out it brings engineering, analytics and machine learning into one platform, which simplifies overall data architecture. the biggest strength is the lakehouse approach ., it combines the flexibility of a data lake with the reliability of a data ware house, this helps to avoid maintaining separate system for storage and analytics, i also like how well it handles large scale processing using spark, whether its batch or steaming data, it performs consistently when configured properly. collaboration is another strong point, teams can work together in notebooks, share logic, and reuse code easily, which improves productivity across departments. the UI is designed for well, notebooks are clean and flexible and switching between SQL , python and scala is smooth. it integrates well with AWS , Azure and GCP and Airflow. performance is strong for large scale workloads . the AI features like Genie is very useful. **What do you dislike about Databricks?** the biggest concern is cost control, if clusters are not managed properly or left running longer than needed, cost can increases faster than expected, auto scaling is helpful but without monitoring , it can still lead to higher usage. sometimes starting cluster can take time, especially when you just want to run quick tests or small jobs, this can slow down development and reduce productivity during short tasks. when something fails in a pipeline or job, debugging is not always easy, logs can be detailed, but tracing the exact issues in complex workflows can take time. **What problems is Databricks solving and how is that benefiting you?** it mainly solves the problem of handling large scale data processing and unifying different data workloads in one platform, earlier building and maintaining ETL pipelines require multiple tools and a lot of manual effort, with databricks i can build, run and manage pipelines in one place using spark, which simplifies the overall process, processing big datasets used to require heavy infrastructure setup, databricks handles this using distributed computing, so i can process large amount of data efficiently without worrying about scaling manually.in traditional setup, we needed separate tools for data engineering , analytics , machine learning, it brings all this into one platform, with shared notebooks and a unified workspace, team can collaborate more easily share code, and work on the same data. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has helped you unify different data workloads and simplify the process of building and managing pipelines. It's great to know that it has streamlined your data processing and collaboration within your team. Thank you for sharing such a comprehensive view of the specific problems Databricks has solved for you. ### 3. Perfect for Cross-team Collaboration and Intensive Data Applications **Rating:** 5.0/5.0 stars **Reviewed by:** Artemij V. | Data Science Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** May 04, 2026 **What do you like best about Databricks?** The UX is one of the strongest parts. The notebook experience is clean and intuitive, collaboration is straightforward, and moving between exploration, experimentation, and production workflows feels seamless. It has enough flexibility for advanced users while still being approachable enough that onboarding new team members is fast. People can usually become productive quickly without spending weeks learning platform-specific quirks. The integrations are also excellent. It works smoothly with the broader cloud ecosystem and connects well with data sources, orchestration tools, model serving infrastructure, and external systems. That interoperability makes it much easier to move from prototype to deployed pipeline without constantly rebuilding connectors or managing glue code. Performance has been consistently strong, especially when working with distributed workloads and large-scale feature engineering. Spark optimization, cluster management, and managed infrastructure significantly reduce operational overhead, which lets me focus more on model development and analysis rather than environment tuning. For iterative experimentation, spin-up times and overall responsiveness are noticeably better than many alternative managed platforms. **What do you dislike about Databricks?** One area where Databricks could improve is pricing. The platform delivers strong capabilities, but costs can escalate quickly for high-frequency or real-time workloads. For use cases involving continuously running low-latency tick pipelines, streaming market data, or iterative model retraining, the pricing can become fairly steep relative to the infrastructure being consumed. It sometimes feels like there’s a meaningful premium for convenience and managed orchestration, which can make cost optimization a constant consideration. The AI integration is another area that still feels somewhat uneven. While there’s a clear push toward positioning the platform as an end-to-end AI/ML environment, some of the newer AI-focused features feel more like ecosystem additions than deeply integrated workflow improvements. In practice, there are still cases where custom tooling or external frameworks provide more flexibility and transparency, particularly for specialized model development, experimentation, and real-time inference use cases. There can also be some complexity around tuning clusters and managing costs efficiently at scale. While the abstractions are helpful, getting the best performance-to-cost ratio sometimes requires deeper platform knowledge than the “fully managed” positioning might imply. Overall, the platform is very strong technically, but pricing for always-on data-intensive workloads and the maturity of some AI-native capabilities are the two biggest areas where I’d like to see improvement. **What problems is Databricks solving and how is that benefiting you?** Databricks solves one of the biggest challenges in modern data work: bringing together data access, large-scale processing, and collaborative development in a single environment. For my work, the biggest benefit is real-time collaboration. It allows multiple people to work against the same datasets, notebooks, and pipelines without the usual friction of fragmented tooling or environment inconsistencies. That significantly speeds up experimentation, iteration, and knowledge sharing across projects, especially when moving quickly on model development or analyzing fast-changing data. It also solves the challenge of scalable data access and processing. Working with high-volume time-series and transactional datasets requires infrastructure that can process large amounts of data efficiently without constant operational overhead. Databricks abstracts much of that complexity, making it possible to focus on analysis, feature engineering, and model development rather than spending time managing infrastructure. The practical benefit is faster iteration cycles. I can move from raw data exploration to model experimentation and deployment much more quickly, which is especially valuable when working on real-time analytics, forecasting pipelines, and production-facing ML systems where speed of iteration directly impacts outcomes. Overall, it reduces engineering friction and makes large-scale collaborative data work significantly more efficient, which translates into faster development, better experimentation, and more reliable deployment of data products. **Official Response from Jess Darnell:** > We appreciate your thorough review of Databricks and are pleased to hear that the platform has been instrumental in enabling cross-team collaboration and intensive data applications for your work. Your feedback on pricing and AI integration is valuable, and we are continuously striving to enhance these aspects to provide a more seamless experience for our users. ### 4. Genie Code and Inline Assistant Dramatically Boosted My Debugging Productivity **Rating:** 4.5/5.0 stars **Reviewed by:** Shyam s. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Genie code and the inline Assistant were the most helpful tools for me on my project. They helped me debug a 2k-line codebase and clearly explained why I wasn’t getting accurate data. It also provided a query to run in my source system (SQLMI). By running the discrepancy script in parallel on the source and target, I was able to debug the entire code much faster and improve my productivity. Overall, it cut my work time from about 8 hours down to around 1 hour. **What do you dislike about Databricks?** In Delta Sharing, there isn’t a catalog-level SELECT permission, and I sometimes think having that would be helpful. Also, when I use the Genie code inside a VM, it can make the website unresponsive at times. These are areas that could be improved. **What problems is Databricks solving and how is that benefiting you?** In one of our claims-processing migration projects, the client needed near real-time data availability for downstream applications. Previously, the architecture used Amazon Redshift as the data warehouse, with Jasper and Sisense consuming the data for reporting and analytics. However, that setup didn’t support real-time or near real-time streaming efficiently, which led to delays in data availability for downstream systems. After migrating the platform to Databricks, we were able to substantially improve the data pipeline architecture. We implemented streaming along with optimized ETL pipelines, reducing the data refresh cycle to about 30 minutes. We also created a dedicated view that retains data from the previous run, so downstream systems always have a consistent dataset available while the next pipeline execution is still in progress. Before, we struggled with delayed refresh cycles and a limited ability to meet near real-time data needs in our Redshift-based architecture. After moving to Databricks, we enabled faster ETL processing and improved near real-time data availability. As a result, we reduced ETL refresh time to roughly 30 minutes and enabled near real-time access for downstream tools like Jasper and Sisense. Reliability also improved because the stable view continues to serve the previous run’s data during pipeline updates. Finally, the overall architecture became simpler by consolidating processing and analytics capabilities within Databricks. Overall, Databricks helped us build a more scalable and efficient near real-time data processing platform, significantly improving the timeliness and reliability of analytics for the claims-processing workflow. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks' architecture is benefiting you. We designed our platform to address the challenges of managing structured and unstructured data, and it's great to hear that it's making a positive impact on your analytics and machine learning workflows. ### 5. The Unified Data Platform That Actually Delivers **Rating:** 5.0/5.0 stars **Reviewed by:** Janakiraman K. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks has transformed how our team handles end-to-end data workflows. A few standouts: UI/UX: The notebook interface is intuitive, and the SQL editor feels polished which switching between Python, SQL, and Scala in the same workspace saves constant context-switching. Integrations: Native connectors to Azure, Unity Catalog, and Delta Sharing mean we spend less time on plumbing. Lakehouse Federation lets us query external sources without moving data, which was an unexpected win. Performance: Delta Lake's auto-optimization and liquid clustering noticeably reduced our query times. Photon engine on heavy aggregations is a game-changer for near real-time dashboards. Pricing/ROI: The DBU model takes getting used to, but consolidating our data warehouse, ETL, and ML tooling into one platform cut our overall infrastructure spend significantly. Support/Onboarding: Databricks Academy and the built-in documentation made onboarding new engineers faster. The community forum is surprisingly active for niche questions. AI/Intelligence: Genie (AI/BI) lets business users ask questions in plain English and get accurate results reducing ad hoc requests to our data team by a noticeable margin. Databricks Assistant inside notebooks also accelerates code generation and debugging. **What do you dislike about Databricks?** While Databricks is powerful, there are real friction points worth mentioning: UI/UX: The interface can feel overwhelming for new users have the navigation between Workspaces, Catalogs, and SQL Warehouses isn't always intuitive. Folder and notebook organization could be more structured out of the box. Integrations: Some third-party connectors still require manual configuration and custom code. Lakehouse Federation is promising but occasionally inconsistent with certain source systems, needing extra troubleshooting. Performance: Cluster startup times remain a pain point cold starts on interactive clusters can disrupt fast-paced workflows. Serverless compute helps but isn't universally available across all features yet. Pricing/ROI: The DBU-based pricing model lacks transparency for newer teams. It's easy to rack up unexpected costs without careful cluster policies and monitoring in place. A more straightforward cost estimator would help significantly. Support/Onboarding: Enterprise support response times can be slow for non-critical tickets. For complex architectural issues, getting to the right expert often takes multiple escalations. AI/Intelligence: Genie works well for standard queries but struggles with complex multi-table logic or domain-specific terminology without significant fine-tuning. The Databricks Assistant inside notebooks occasionally generates outdated or incorrect API suggestions. **What problems is Databricks solving and how is that benefiting you?** Here's a natural, story-driven answer following the "before/after/result" format: Before Databricks, our data landscape was fragmented and separate tools for ETL, warehousing, and ML meant duplicated pipelines, inconsistent data definitions, and significant engineering overhead just to maintain the plumbing. Data Unification: We struggled with siloed data across multiple source systems. Now, with Unity Catalog and the Medallion architecture (Bronze/Silver/Gold), we have a single governed layer that all teams trust reducing data reconciliation effort by nearly 40%. Pipeline Reliability: Building and maintaining metadata-driven pipelines used to require custom frameworks. Databricks' Lakeflow and Delta Live Tables gave us incremental and full-load capabilities out of the box, cutting pipeline development time significantly. Self-Service Analytics: Business teams constantly depended on engineers for ad hoc queries. With Genie (AI/BI), stakeholders can now ask plain-English questions against curated gold tables reducing ad hoc data requests to our team noticeably week over week. Cloud Cost Control: We previously ran always-on clusters without visibility into spend. Serverless compute and cluster policies now let us right-size workloads, resulting in measurable infrastructure cost reduction. Faster Onboarding: New engineers previously took weeks to get productive. With Databricks Assistant, notebook templates, and centralized Unity Catalog documentation, ramp-up time has dropped considerably. Overall: Databricks essentially replaced 3-4 separate tools with one cohesive platform the ROI isn't just in cost savings, it's in the speed and confidence with which we now deliver data products to the business. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks valuable for data engineering, analytics, and machine learning. Thanks for sharing your feedback! ### 6. Scalable, All-in-One Environment with Some Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Antonio V. | Data & AI Consultant, Mid-Market (51-1000 emp.) **Reviewed Date:** April 28, 2026 **What do you like best about Databricks?** I like Databricks for its scalability and all-in-one environment for data engineering, analytics, and machine learning. It allows me to process large datasets efficiently while keeping workflows organized in one platform. The scalability is very valuable because it lets me handle growing data volumes and complex workloads without performance issues. As projects expand, the platform can scale resources efficiently. **What do you dislike about Databricks?** Some features can have a learning curve, especially for new users working with advanced configurations or cluster management. The interface could also be more intuitive in certain areas. The setup was relatively smooth for core features, but some advanced settings like cluster optimization, permissions, and integrations required more time and technical knowledge. **What problems is Databricks solving and how is that benefiting you?** Databricks solves major data management and analytics challenges by efficiently handling large datasets, simplifying ETL processes, and centralizing workflows. Its scalability allows me to manage growing data volumes without performance issues, ensuring resources scale efficiently as projects expand. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks scalable and appreciate its all-in-one environment for data engineering, analytics, and machine learning. We understand that some features may have a learning curve, and we are continuously working to improve the platform's usability and intuitiveness. ### 7. Performance with Spark and collaborative notebooks that make the data flow more efficient **Rating:** 5.0/5.0 stars **Reviewed by:** Homero F. | Professor particular, Mid-Market (51-1000 emp.) **Reviewed Date:** April 19, 2026 **What do you like best about Databricks?** What I like most is the performance in processing large volumes of data with Spark, the collaborative notebooks that facilitate teamwork, and the integrations with AWS and BI tools, which make the entire data flow more efficient. **What do you dislike about Databricks?** The cost can be high depending on the usage, and some parts of the interface, such as cluster and job configuration, are not very intuitive at first. Additionally, the learning curve can be somewhat steep for new users. **What problems is Databricks solving and how is that benefiting you?** Databricks solves problems of processing large volumes of data, integrating different sources, and developing AI models in a single environment. This improves our workflow, reduces processing time, and centralizes everything on the platform. Integrations with AWS and other tools facilitate implementation, and the support along with the documentation help with adaptation. Additionally, the AI resources allow for creating, training, and testing models more quickly and efficiently. **Official Response from Jess Darnell:** > Thank you for your positive feedback! ### 8. Powerful Unified Analytics with Seamless Governance and Effortless Scaling **Rating:** 4.5/5.0 stars **Reviewed by:** Akhil S. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its powerful and unified analytics ecosystem. Features like Unity Catalog and Metastore make data governance and access control seamless, while the Lakehouse architecture combines the best of data lakes and warehouses. PySpark support, dbutils, and collaborative workspaces make development efficient, and serverless compute simplifies scaling without infrastructure overhead. **What do you dislike about Databricks?** What I dislike about Databricks is the slow startup time of all-purpose clusters, which can interrupt workflow and reduce productivity. Additionally, Git integration can feel a bit sluggish at times, especially during commits or syncing, making version control less seamless than expected. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of managing end-to-end data workflows by providing a unified platform for data engineering, data science, and analytics. It allows seamless data processing, transformation, and model development within a single environment. This benefits me by simplifying my workflow as both a data engineer and data scientist, reducing the need to switch between tools. Additionally, its integration with Azure Data Factory enables smooth job orchestration and triggering for higher environments, making deployments more efficient and reliable. **Official Response from Jess Darnell:** > We're pleased to hear that Databricks is simplifying your data workflows and providing seamless integration with Azure Data Factory. We take note of your concerns about slow startup times and Git integration, and we are committed to optimizing these aspects to ensure a smoother experience for our users. Your input helps us prioritize enhancements that align with our users' needs. ### 9. Databricks: Unified Lakehouse Platform with Powerful Spark Performance **Rating:** 4.0/5.0 stars **Reviewed by:** Tejaswini R. | Data Management Specialist, Mid-Market (51-1000 emp.) **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** i am working as a Data management specialist and using databricks regularly for handling data pipelines, large scale data processing, and governance tasks, i like most is that databricks provides a single unified platform for data engineering , analytics and AI , instead of using multiple tools. everything is available in one place, the lakehouse architecture is very useful because it combines data warehouse and data lake capabilities, so we can manage both structured and unstructured data efficiently. performance is very strong, especially with apache spark, it can process very large datasets quickly. i also like the collaborative notebooks where teams can work together using SQL, python or scala. **What do you dislike about Databricks?** one issue is that it has a steep learning curve, especially for new users who are not familiar with spark or distributed systems. cost management can also be challenging , it clustered are not optimized properly it can become expensive, sometimes too many features and configuration can makes it complex to manage for smaller teams. sometimes the platform feel complex. with many feature and configuration which can be difficult for smaller teams to manages. it it a powerful platform, but complexity and cost control are the main challenges in daily use. **What problems is Databricks solving and how is that benefiting you?** databricks solves the problem of managing large scale data processing and multiple data tools in a single platform, before using databricks data was spread across different system. and we has to use separate tools for ETL, storage and analytics, this made workflow complex and difficult to manage, databricks brings everything together in one place, so we can build data pipeline , process large datasets, and run analytics without switching tools. it also handles big data efficiently using distributed processing, which reduces processing time and improves performance, for me it has made data workflows more organized, reduces manual effort, and improved data reliability. it helps in faster data processing, better collaboration and more efficient data management. **Official Response from Jess Darnell:** > It's great to hear that Databricks has helped centralize your data processing and tools, making your workflows more organized and efficient. We're committed to providing a platform that simplifies data management and improves collaboration for our users. We understand that the learning curve and cost management can be challenging, especially for new users and smaller teams. We're continuously working to improve user experience and provide cost-effective solutions for our customers. ### 10. Seamless, Collaborative Platform That Scales for Data Engineering and ML **Rating:** 4.0/5.0 stars **Reviewed by:** Krish G. | student, Small-Business (50 or fewer emp.) **Reviewed Date:** April 15, 2026 **What do you like best about Databricks?** Databricks' ability to seamlessly integrate everything is what I find most appealing. When working on actual projects, it really makes a big difference that you don't have to switch between several tools for data engineering, analysis, and machine learning. The collaborative element is very noteworthy. Teams may easily collaborate without things becoming messy thanks to the notebooks' fluid and dynamic feel. For significant data work, it resembles Google Docs almost exactly. I also really like how efficiently it manages large amounts of data without making it seem difficult. Even when working with large datasets, the platform feels user-friendly and can be scaled up when necessary. Additionally, it makes perfect sense from an AI/ML standpoint. You are able to construct, **What do you dislike about Databricks?** Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. Databricks can initially feel a little overwhelming, which is something I don't like. Clusters, notebooks, jobs, workflows—there's a lot going on, and if you're new, it takes some time to truly grasp how everything works together. Cost control is another drawback. It is undoubtedly strong, but expenses might quickly increase if you are careless with cluster usage or auto-scaling settings. To keep everything under control, you need to exercise some self-control and keep an eye on things. **What problems is Databricks solving and how is that benefiting you?** The fragmentation issue in the data and AI workflow is primarily resolved by Databricks. In the past, data storage, processing, analysis, and machine learning were usually done using different tools, and getting them all to cooperate was frequently difficult and time-consuming. Databricks eliminates a lot of the friction by combining all of it into a single platform. That makes the developing process much more seamless for me. I don't have to worry about compatibility problems or waste time switching between environments. I can perform transformations, clean data, and create models all in one location, which reduces setup time and maintains organization. It also addresses the difficulty of handling massive amounts of data. I can rely on its distributed computing capabilities to manage demanding workloads rather than worrying about infrastructure or performance optimization from scratch. This allows me to concentrate less on resource management and more on finding a solution to the real issue. Collaboration is another major issue it resolves. Sharing code, findings, and experiments can get disorganized in team environments. Because everything is consolidated with Databricks, it's simpler to work together, monitor changes, and maintain alignment. All things considered, it helps me by cutting down on complexity, saving time, and allowing me to concentrate more on developing solutions—whether they be analytics, machine learning models, or data pipelines—instead of handling the overhead of maintaining numerous tools and platforms. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' seamless integration and collaborative features appealing. We understand that the platform may feel overwhelming initially, but we offer comprehensive resources and support to help users get up to speed. Regarding cost control, we recommend leveraging our documentation and best practices to optimize cluster usage and auto-scaling settings. Your feedback is appreciated and we are committed to continuously improving the user experience! ### 11. Databricks as a Hands On Data Engineer: Solving Real World ETL, Governance, and Lakehouse Challenges **Rating:** 5.0/5.0 stars **Reviewed by:** KAVIN P. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 08, 2026 **What do you like best about Databricks?** I believe the most attractive thing about Databricks lies in its all-in-one nature, which makes data management easier. Previously, when I used several tools for data-related activities, the experience was not great but here everything seems to be interconnected and straightforward. The ability to utilize notebooks, especially when working with PySpark, is another advantage of Databricks that i like the core. The tool allows quickly executing changes and modifications without excessive preparation. It also positively impacts the process of collaboration among my team who can simultaneously work on their projects and monitor the overall progress. However, version control can sometimes appear a bit unclear in my view. In performance, Databricks seem efficient for me at handling big data and operating smoothly without delays. Cluster scaling occurs automatically, allowing me and my team to save time on the infrastructure level. Therefore,it is easy as no additional planning and adjustments are required. There are minor issues with the UI, which sometime work slowly. but at overall due to is super other aspects like easy methods in implementing and integrating things it encourages me to utilize Databricks frequently. **What do you dislike about Databricks?** One aspect of Databricks that i dislike is its UI. As you spend longer in using the tool, moving between notebooks and clusters becomes annoying at times. The other problem is the costs that can faster sum up when we are not cautious. Unnecessary clusters may be running for a longer period than required and without the me or my teams knowledge, thereby increasing up the costs in our projects. There is also complexity of debugging the errors, which are difficult at times as it involves spending extra effort trying to find out where things might have been wrong mainly when dealing with complex pipelines. At times, there are some discrepancies with regards to customer service which takes us somewhere where we need not to be. **What problems is Databricks solving and how is that benefiting you?** The most important issue that Databricks resolves is the issue of working with large volumes of data and maintaining consistency. Previously, there were separate processes for data engineering, analytics, and machine learning operations, requiring separate tools and made it difficult for me to handle but now these all are in one place, another one critical issue solved by Databricks is the issue of processing large data volumes. Utilizing the Spark, and distributed computing allows it to perform the tasks that were extremely slow on legacy systems I worked with. This has helped speed up my pipeline, although some time the delays occur.Collaboration is also another problem that Databricks addresses. Multiple users can collaborate on the same notebook or data sets. Collaboration previously was confusing, and now it is easy and good and easy and easly understandable and mainly easy sharing notebooks and assets.Scalability is another issue resolved by Databricks; there is no need to pay attention to infrastructure management. Cluster scaling depends on user requirements, saving time. Previously, it was necessary to pay more attention to the configuration of the infrastructure. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' all-in-one nature and interconnectedness beneficial for data management to help your team save time. We appreciate your feedback on the advantages of utilizing notebooks and the efficiency in handling big data. ### 12. Unified Databricks Workspace That Streamlines Collaboration and Complex Data Workflows **Rating:** 4.0/5.0 stars **Reviewed by:** Neeraj Kumar N. | AI Data Specialist | Transcription & Annotation Expert | AI Model Training at Sigma AI, Mid-Market (51-1000 emp.) **Reviewed Date:** April 12, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning into one unified workspace. I find collaboration much easier with shared notebooks, and the seamless integration with big data tools saves me time. It simplifies complex workflows while still offering powerful capabilities when I need them. **What do you dislike about Databricks?** One thing I dislike about Databricks is that it can feel expensive, especially for smaller projects or teams. I also find cluster configuration and cost management a bit complex at times. The interface, while powerful, can be overwhelming for beginners, and debugging distributed jobs isn’t always as straightforward as I’d like. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the challenge of handling large-scale data processing, analytics, and machine learning in one place. For me, it removes the hassle of managing separate tools and infrastructure. I benefit by working more efficiently, collaborating easily with my team, and turning complex data into useful insights faster, with less operational overhead overall. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' unified workspace and collaboration features valuable for your work. We understand your concerns about cost and complexity, and we're continuously working to improve in these areas. ### 13. Reliable data platform with powerful pipeline support **Rating:** 4.5/5.0 stars **Reviewed by:** Chandhuru B. | Data Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 06, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it brings data engineering, analytics, and machine learning together in one clean workspace. It saves time, makes collaboration easier, and helps teams move faster with large data. **What do you dislike about Databricks?** What I dislike about Databricks is that Auto Loader can become frustrating when source data changes frequently, especially if column names or datatypes shift without warning. For example, a field like customer_id may suddenly come in as cust_id, or a column that was previously a string may start arriving as an integer, which can cause schema drift and break downstream processing. I also find it inconvenient when schema inference is not fully accurate, such as when nested JSON or semi-structured data is read incorrectly, because it then requires extra manual fixes and maintenance to keep pipelines running smoothly. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of building and managing data pipelines at scale without so much manual effort. It helps with reliable ingestion, schema evolution, and orchestration, so teams can process data faster and keep pipelines more stable even when source files change. For me, that means less time spent fixing broken jobs and more time focusing on transforming and using the data. It also benefits me by making batch and streaming workflows easier to manage in one platform, which is especially useful when data keeps changing. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a reliable platform for data engineering, analytics, and machine learning. We understand the frustration with Auto Loader when dealing with frequently changing source data. We are continuously working to improve schema inference accuracy and handling of nested JSON or semi-structured data to minimize manual fixes and maintenance for our users. ### 14. Databricks: Unified Platform for Data Processing and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Banu Prakash M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks brings everything into one place, making it unnecessary to use different tools for data processing, analytics, and pipeline work. It handles large data well, and we don't have to worry about managing clusters manually. Additionally, Databricks handles collaboration and experimentation well, making it easy to try out new things. **What do you dislike about Databricks?** In my point of view, the one area that can be improved is cost management. If clusters aren't monitored carefully, costs can increase faster than expected. One improvement that would help is better visibility into costs at a more detailed level. More built-in alerts or recommendations when costs start increasing unexpectedly would also be helpful. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us handle large datasets and build data pipelines. It simplifies data processing, transforming, and analysis using Spark and SQL, all in one place. It solves the problem of slow data processing spread across systems, managing infrastructure automatically and facilitating collaboration and experimentation. **Official Response from Janelle Glover:** > We're thrilled to hear that Databricks has been beneficial for handling large datasets and simplifying data processing and analysis for you. We appreciate your feedback on cost management and will explore ways to enhance cost visibility and provide better monitoring tools. ### 15. A Reliable Workhorse for Data Engineering and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** Supriya M. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** The unified platform approach is what I appreciate most. Having notebooks, data engineering pipelines, ML workflows, and SQL analytics all in one place saves a ton of time instead of juggling multiple tools. The collaborative notebooks make it easy to share work with teammates, and the cluster management has gotten a lot smoother over time. Delta Lake integration is also a huge plus for keeping our data reliable and consistent. **What do you dislike about Databricks?** The cost can get out of hand pretty quickly if you're not careful with cluster sizing and uptime. It's not always obvious how to optimize spending, and the pricing model feels complex. The learning curve for new team members is also steeper than I'd like, especially for people who aren't already familiar with Spark. Sometimes the UI can feel sluggish when working with larger notebooks, and debugging job failures could be more straightforward. **What problems is Databricks solving and how is that benefiting you?** Databricks helps me resolve complex ETL pipeline failures and persistent data quality issues in supply chain analytics by unifying batch and streaming processing from SAP systems with Delta Live Tables. It also removes a lot of the infrastructure management headaches thanks to auto-scaling clusters, so I can stay focused on writing code for multi-terabyte workloads instead of constantly worrying about cluster sizing. For my manufacturing data projects, Databricks accelerates development cycles from weeks to days via collaborative notebooks and DLT pipelines, enabling faster Power BI reporting and stakeholder decisions. Unity Catalog centralizes governance across Azure and SAP sources, preventing schema drift that plagued prior Hive-based lakes. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of the unified platform approach and the time-saving features of Databricks. We understand your concerns about cost management and the learning curve, and we're continuously working to simplify our pricing model and improve the onboarding experience for new team members. It's great to hear how Databricks is helping you resolve complex ETL pipeline failures and accelerating development cycles for your manufacturing data projects. ### 16. All-in-One Powerhouse with Room for Pricing Clarity **Rating:** 4.5/5.0 stars **Reviewed by:** Thoufeeq A. | DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like that Databricks is an all-in-one powerhouse where I can do multiple works in one place. It's powerful to manage data from multiple sources and have it in a single UC to manage permissions with row-level security. I also appreciate that I can create experiments, run multiple models, and select the best one from logs, which was difficult on other platforms. Once I learned the setup, it's been easy and comfy to work with. **What do you dislike about Databricks?** I find it difficult to use the calculator to determine CPU serving endpoint prices because the documentation doesn't explicitly explain this. It only mentions 1 concurrency equals 1 DBU on the Azure page, which isn't clear. The pricing calculator has a single option for serving endpoints, labeled as medium with four DBU, but lacks separate options for GPU or CPU and their concurrency, making it hard to understand how it works properly. Initially, I also felt it was very tough to learn Databricks and manage deployments of workspaces, although it became easier over time. **What problems is Databricks solving and how is that benefiting you?** Databricks consolidates multiple tools into one platform, making it powerful and convenient. I can manage permissions with row-level security and easily run experiments to select the best models, all in one place. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We understand your concerns about the pricing calculator and will take your feedback into consideration to improve the clarity of our documentation. ### 17. Databricks Lakehouse Powerhouse with Unity Catalog and Fast Photon SQL **Rating:** 4.0/5.0 stars **Reviewed by:** Vidhyadar R. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I really value how the platform brings data lakes and warehouses together into one place. It makes managing data much easier, and the SQL performance is very fast thanks to the Photon engine. I also like the collaborative notebooks because they allow me to work with both SQL and Python seamlessly in a single environment. **What do you dislike about Databricks?** The cost can be high, and the DBU billing system is quite complex to track. I also found that there is a significant learning curve when it comes to Spark and configuring clusters. For smaller, quick tasks, the setup time and technical overhead can sometimes feel like a bit too much. **What problems is Databricks solving and how is that benefiting you?** ​It solves the issue of having data scattered everywhere. I love that I can switch between SQL and Python in the same spot, and the processing speed is top-notch. It’s been a game-changer for building out our financial models quickly without the usual lag. **Official Response from Janelle Glover:** > We appreciate your feedback on the benefits of Databricks, such as the centralized data management and the ability to work with SQL and Python in a single environment. We understand your concerns about cost and the learning curve, and we're actively working to enhance the platform to better meet your needs. ### 18. Unified Data Engineering, Science, and Analytics in One Collaborative Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Sivabalan A. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** What I appreciate most about Databricks is its ability to unify data engineering, data science, and analytics on a single platform. The collaborative environment—especially the notebooks and integrated workflows—makes it much easier for teams with different skill levels to work together without constant context-switching. Another highlight is the integration with popular tools and cloud services that are widely used in the market today, which makes it easier to move data between them. The performance monitoring and job scheduling features help maintain visibility over pipelines, and the Delta Lake support for reliable data management has also been very useful. **What do you dislike about Databricks?** Cost management is one area that could be improved. While Databricks offers autoscaling and flexible cluster options, it’s easy for resource usage to escalate unexpectedly, especially with large datasets and long-running jobs. Keeping costs predictable often requires careful oversight and a solid understanding of the platform’s pricing model. Additionally, some of the more advanced features—such as fine-grained access controls and more complex job orchestration—can feel less intuitive. The documentation is extensive, but it occasionally leaves gaps that end up requiring trial and error. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses several key challenges in modern data workflows, particularly around scalability, data reliability, and collaborative analytics. One major problem it solves is managing and processing large-scale datasets efficiently. By leveraging Apache Spark’s distributed computing framework, Databricks enables parallelized ETL pipelines and large-scale data transformations that would be impractical on traditional infrastructure. Another challenge is ensuring data consistency and reliability across pipelines. With Delta Lake, Databricks provides ACID-compliant storage, versioned tables, and schema enforcement, which reduces data errors and simplifies data governance. This is especially beneficial when multiple teams are working on different stages of data pipelines at the same time. Databricks also helps solve the problem of fragmented workflows for data scientists and engineers. Its unified environment supports multiple languages (Python, SQL, R, Scala) and includes integrated machine learning with MLFlow, making it easier to collaborate and move from data preparation to analytics and ML in one place. **Official Response from Janelle Glover:** > It's great to hear how Databricks is helping address scalability, data reliability, and collaborative analytics challenges for your team. We appreciate your feedback on cost management and advanced feature usability. We are continuously working to improve our pricing transparency and enhance the user experience for all our features. ### 19. All-in-One Platform That Helps Us Iterate Fast and Deploy with Confidence **Rating:** 5.0/5.0 stars **Reviewed by:** Vijayaramuprawin V. | Sr. Cloud and DevOps Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** We use Databricks daily as our core data platform for building and running pipelines across a medallion architecture, from extracting data out of SAP and Arkieva all the way to reporting-ready datasets. The notebook experience is intuitive, the feature set is massive, and Asset Bundles have made our CI/CD story with Azure DevOps really solid. Integration with cloud services was smooth, and once things are set up they just work. The learning curve can be steep for newer team members, especially around things like Unity Catalog and DABs, and costs can creep up if you're not staying on top of cluster configurations. Support is decent and the docs are strong enough that we rarely need to open a ticket. Overall, it's a powerful platform that does a lot under one roof, and it's hard to imagine our data engineering workflow without it. **What do you dislike about Databricks?** The cost can creep up fast if you're not careful with cluster sizing and job configurations, so it takes some effort to keep things optimized. Also, the learning curve for newer team members can be steep, especially around things like Asset Bundles, Unity Catalog, and getting the CI/CD pieces wired up properly. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of having fragmented data spread across multiple systems like SAP and Arkieva by giving us one unified platform to extract, transform, and serve it all. That means our business teams get clean, reliable, reporting-ready data without us having to juggle a bunch of separate tools, and we can deploy and manage everything consistently across environments with confidence. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your data engineering workflow and providing a powerful platform for your needs. We appreciate your feedback on the learning curve and cost considerations, and we're continuously working to improve in these areas. ### 20. Streamlined, Collaborative Data Workflows with Powerful Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Dharun T. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it streamlines the entire data workflow by bringing processing, analysis, and machine learning into one platform. The collaborative notebook environment makes it easy to share code, context, and reasoning with teammates, which helps everyone stay aligned. It also performs strongly on large datasets while abstracting away most of the cluster management, so I can focus on solving the problem rather than dealing with infrastructure. On top of that, centralized access control and clear visibility into data usage support responsible data governance, offering a solid balance between power and ease of use. **What do you dislike about Databricks?** Databricks has a few downsides, although many of them feel more like trade-offs than outright negatives. My biggest concern is cost: if clusters aren’t managed carefully, expenses can climb quickly, even though the platform can scale very efficiently when it’s tuned properly. There’s also a real learning curve with Spark and distributed computing concepts, and debugging or performance tuning can be more involved than with simpler tools. Lastly, because it’s a managed service, you give up some low-level control compared with self-hosted systems, but the upside is that it takes a lot of the operational and infrastructure work off your plate. **What problems is Databricks solving and how is that benefiting you?** Because my client needs secure, reusable code, Databricks helps us write Python efficiently while applying OOP principles and design patterns. It also makes it straightforward to extend functionality over time and build custom code that interacts with APIs and databases. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a powerful and streamlined platform for collaborative data workflows. We understand the concerns about cost management and the learning curve associated with distributed computing concepts. We continuously work to improve our platform and provide resources to help users optimize their usage and overcome challenges. ### 21. Databricks: All-in-One Solution for Data and Analytics **Rating:** 5.0/5.0 stars **Reviewed by:** FABIN P. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** What I like most about Databricks is that it brings everything into one place, making it easy to work on data, build models, and manage workflows. It helps teams collaborate easily in real time. It also works very fast with large data using Apache Spark, and features like automation and Delta Lake make handling big data much simpler. **What do you dislike about Databricks?** One thing I dislike about Databricks is that it can be expensive, especially for large workloads. Sometimes the interface and setup can feel complex for beginners. Also, managing clusters and configurations can take some effort if you’re not very familiar with it. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of handling large amounts of data efficiently. It brings data engineering, analysis, and machine learning into one platform. This removes the need to use multiple tools. It helps in faster data processing using Apache Spark. It makes collaboration easier for teams. It simplifies building and managing data pipelines. It improves data reliability with features like Delta Lake. It reduces manual work through automation. It saves time and effort in daily tasks. Overall, it helps me work faster and more efficiently with data. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your data engineering workflow and providing a powerful platform for your needs. We appreciate your feedback on the learning curve and cost considerations, and we're continuously working to improve in these areas. ### 22. From Hive Chaos to Unity Catalog - Worth Every DBU **Rating:** 5.0/5.0 stars **Reviewed by:** Balakumaran R. | Data Team Lead, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** Unity Catalog has been the single biggest value-add for our enterprise migration. We moved from a Hive Metastore architecture to Unity Catalog and gained centralized governance, lineage tracking, and fine-grained access control across all our data assets without bolting on third-party tools. For a multi-domain organization (finance, manufacturing, supply chain, procurement), having one catalog that enforces consistent naming and permissions across bronze, silver, gold, and platinum layers saved us weeks of manual policy work. UI/UX: The notebook experience with inline Spark SQL and PySpark, combined with the workspace file browser, makes it straightforward for our team to develop and test transformations iteratively. The SQL editor for ad-hoc queries against Unity Catalog tables is clean and responsive. Integrations: Native Delta Lake support means we don't manage format conversions. The Azure Key Vault integration via secret scopes (dbutils.secrets.get) keeps credentials out of code. ADF integration for orchestration in our V1 environment was seamless, and Databricks Asset Bundles (DAB) for V2 deployment give us a clean CI/CD path with databricks.yml configs targeting dev/qa/prod without custom scripting. Performance: Switching to CTEs over temp views in our Gold notebooks reduced cluster memory pressure noticeably. The ability to right-size clusters per environment (1 worker for dev, 3 for production) with Standard_D4ds_v5 nodes keeps costs predictable while maintaining performance for our batch ETL workloads. Pricing/ROI: The pay-as-you-go compute model paired with single-user security mode clusters means we're not over-provisioning. Consolidating our ETL, governance, and BI serving layer into one platform eliminated licensing for separate catalog, orchestration, and data quality tools. AI/Intelligence (Genie): Genie Spaces have been an unexpected win. Our business analysts in finance and supply chain can ask natural language questions against curated Gold/Platinum tables without writing SQL. It reduced the number of ad-hoc report requests coming to the data team by giving domain users a self-service path that still respects Unity Catalog permissions. Support/Onboarding: The documentation is thorough, and the skills-based approach to learning (bundles, Unity Catalog, jobs, SQL) maps well to how our team actually works. Onboarding new engineers to the V2 architecture took about half the time compared to V1 because the platform conventions (medallion architecture, asset bundles, catalog naming) are well-documented and consistent. **What do you dislike about Databricks?** UI/UX: The notebook editor still feels behind dedicated IDEs. No native multi-file search, limited refactoring support, and the git integration UI is clunky for teams managing dozens of notebooks across workflow bundles. We ended up doing all real development in VS Code and treating the Databricks workspace as a deployment target, which adds friction. The workspace file browser also doesn't handle folder structures well when you have 50+ notebooks organized by domain there's no filtering, tagging, or favorites. Integrations: Databricks Asset Bundles (DAB) are a step forward, but the documentation has gaps for complex multi-bundle deployments. We run a shared Global_Utilities bundle that other workflow bundles depend on, and getting cross-bundle references to work reliably across dev/qa/prod targets required significant trial and error. The ADF-to-Databricks integration works, but debugging failed pipeline runs means jumping between the ADF monitoring UI and Databricks job runs with no unified view. A tighter handshake between orchestration and compute monitoring would save hours of troubleshooting. Performance: Cluster cold-start times remain a pain point for development workflows. Spinning up a single-node Standard_D4ds_v5 cluster takes 4-7 minutes, which breaks flow when you're iterating on notebook logic. Serverless compute helps but isn't available for all workload types yet, and the cost premium is hard to justify for dev/test environments. Pricing/ROI: The DBU pricing model is opaque for capacity planning. Estimating monthly costs for a project with 30+ scheduled jobs, interactive development clusters, and SQL warehouse queries requires building custom spreadsheets because the built-in cost management tools don't give you a clear forecast by workflow or domain. We've been surprised by cost spikes from jobs that ran longer than expected with no easy way to set per-job budget alerts. Support/Onboarding: Enterprise support response times are inconsistent. Critical issues with Unity Catalog permissions during our migration took 3-5 business days for initial triage, which stalled our deployment timeline. The community forums are helpful for common patterns, but for Unity Catalog edge cases (cross-catalog lineage, complex permission inheritance), the knowledge base is thin. AI/Intelligence: Genie is promising but still rough for production use. It struggles with joins across more than 3-4 tables, sometimes generates incorrect SQL against our Gold layer, and there's no easy way to curate or correct its responses to improve accuracy over time. Our business users got excited, tried it, hit wrong answers on moderately complex questions, and lost trust. A feedback loop where domain experts can flag and correct Genie's outputs would make it genuinely production-ready. **What problems is Databricks solving and how is that benefiting you?** Data Governance Fragmentation → Unified Catalog We struggled with a Hive Metastore environment where table ownership, access control, and lineage were managed through a patchwork of manual documentation and custom scripts. After implementing Unity Catalog, we now have centralized governance across 4 catalog layers (bronze, silver, gold, platinum) spanning 6 business domains. What used to take a full-time data steward to track manually is now enforced automatically through catalog-level permissions and lineage. This cut our access provisioning time from days to under an hour per request. Siloed ETL Logic → Standardized Medallion Architecture Before Databricks, our ETL pipelines were inconsistent — different teams wrote transformations differently, with no shared utilities or patterns. We built a standardized framework (Batch_Utilities.py) with reusable functions for schema validation, merge operations, data quality checks, and audit column management. Every notebook across all domains now follows the same 7-cell structure. This reduced new notebook development time from 2-3 days to roughly 4 hours, and onboarding a new developer to the pattern takes a single afternoon instead of a week. Costly Report Refresh Failures → Reliable Pipeline Orchestration We had recurring issues with Power BI reports pulling stale or incomplete data because upstream jobs failed silently. With Databricks Jobs and metadata-driven pipeline tracking (pipeline status, start/end timestamps logged per run), we now catch failures at the transformation layer before they propagate to reports. Report data freshness issues dropped by approximately 80%, and our finance team stopped scheduling "data verification" meetings that used to consume 3-4 hours per week. Multi-Environment Deployment Chaos → Asset Bundles Deploying notebooks across dev, QA, and production used to involve manual file copies and environment-specific config edits — error-prone and slow. Databricks Asset Bundles gave us declarative databricks.yml configs with variable substitution per target. A deployment that took 45 minutes of manual steps now runs in under 5 minutes via CLI. We deploy with confidence because the same bundle definition is validated before it hits production. Self-Service Analytics Gap → Genie + Platinum Layer Business analysts in supply chain and finance were fully dependent on the data team for any ad-hoc analysis. By building denormalized Platinum tables optimized for reporting and exposing them through Genie Spaces, we enabled self-service querying in natural language. Early adoption has reduced ad-hoc report requests to the data team by roughly 30%, freeing up engineering capacity for new feature development. Cost Visibility → Right-Sized Compute We were over-provisioning clusters because we had no clear view of actual utilization. By standardizing on Standard_D4ds_v5 nodes with environment-specific worker counts (1 for dev/QA, 3 for production) and single-user security mode, we reduced our monthly compute spend by approximately 25% compared to the shared cluster model we ran in V1. **Official Response from Janelle Glover:** > We appreciate your detailed feedback on your experience with Databricks. It's great to hear that Unity Catalog, UI/UX, integrations, performance, Genie, and support/onboarding have positively impacted your enterprise migration. We understand the areas of improvement you've mentioned and will take them into consideration for future enhancements. ### 23. Databricks: Intuitive, Unified Platform with Seamless Integrations and Fast Support **Rating:** 5.0/5.0 stars **Reviewed by:** Sabareeswar K. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** As a data engineer, Databricks has become my go-to platform for end-to-end data work. The ease of use is outstanding notebooks, Delta Live Tables, and Genie all have intuitive interfaces that reduce rampup time significantly. Implementation was smooth thanks to excellent documentation and responsive customer support that actually resolves issues fast. I use it daily, and the sheer number of features from Unity Catalog to AI/BI Genie keeps growing. Integration with cloud storage, BI tools, and ML frameworks is seamless, making it a true unified platform. **What do you dislike about Databricks?** One challenge is the lack of cost transparency at a granular job level it's difficult to pinpoint exactly which pipeline or notebook is driving up DBU consumption without investing in custom monitoring. Auto scaling clusters, while powerful, can silently balloon costs overnight if not carefully configured with proper limits. Additionally, the SQL warehouse tiers can be confusing to choose from upfront, making budget planning tricky for teams. A built in cost allocation dashboard per job or user would be a huge improvement for day to day cost governance. **What problems is Databricks solving and how is that benefiting you?** Databricks has eliminated the silos between our data engineering, analytics, and ML teams. Previously, we juggled multiple tools for ingestion, transformation, and reporting. Now everything lives in one lakehouse. Genie specifically has been a game-changer business stakeholders can ask natural language questions directly against our data without writing SQL, which dramatically reduces ad-hoc request bottlenecks for our engineering team. Decision making is faster, data is more democratized, and we've cut our reporting pipeline overhead by a significant margin. **Official Response from Janelle Glover:** > It's great to hear that Databricks has helped eliminate silos between your data engineering, analytics, and ML teams. We're pleased that Genie has been a game-changer for your business stakeholders. We also understand the challenges you've mentioned regarding cost transparency and auto-scaling clusters. We are continuously working to improve our platform and will take your suggestions into consideration for future enhancements. ### 24. Databricks Makes End-to-End Data Workflows Fast, Collaborative, and Easy **Rating:** 5.0/5.0 stars **Reviewed by:** Karuppusamy V. | Technical Lead, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it simplifies the entire data workflow. Instead of switching between multiple tools for data processing, analysis, and machine learning, everything is available in one place. The notebook environment makes collaboration really smooth it feels natural to work with teammates, share code, and explain logic without extra effort. Another thing I appreciate is the performance. Working with large datasets can usually be painful, but Databricks handles it efficiently in the background. You don’t have to worry much about managing clusters or optimizing everything manually it just works most of the time, which lets you focus more on solving the actual problem rather than dealing with infrastructure. What also stands out is the way it handles data governance and organization. With features like centralized access control and better visibility into data usage, it becomes much easier to manage data responsibly, especially in larger projects. Overall, it gives a good balance between power and ease of use, which is why I enjoy working with it. **What do you dislike about Databricks?** One thing I don’t particularly like about Databricks is that it can get expensive pretty quickly, especially if clusters are not managed properly. If you forget to terminate clusters or run heavy workloads without optimization, costs can spike without much visibility at first. For teams that are still learning or experimenting, this can become a concern. Another downside is that debugging can sometimes feel a bit tricky, particularly when working with distributed jobs. Errors are not always straightforward, and tracing issues across multiple nodes can take extra time compared to working in a simpler local environment. It requires a certain level of experience to quickly understand and fix issues. Also, while the platform is powerful, it has a bit of a learning curve for beginners. Concepts like cluster configuration, job scheduling, and data governance are not always very intuitive at the start. It takes some hands-on time before you feel fully comfortable navigating and using everything efficiently **What problems is Databricks solving and how is that benefiting you?** What Databricks really solves is the problem of handling large-scale data without making the process overly complex. Earlier, working with big data meant dealing with multiple tools, managing infrastructure, and spending a lot of time just setting things up. Databricks simplifies all of that by bringing data engineering, analytics, and machine learning into one place, so the focus shifts more toward solving actual business problems instead of managing systems. It also addresses performance and scalability issues. When working with huge volumes of data, traditional systems often struggle or slow down. Databricks handles this efficiently in the background, allowing workloads to scale without much manual effort. For me, this means I can process large datasets faster and run transformations or queries without constantly worrying about performance tuning. Another big problem it solves is collaboration and data management. In many projects, teams struggle with version control, access management, and keeping data consistent. Databricks makes it easier to collaborate, track changes, and control who can access what. This helps me work more smoothly with others, reduces errors, and ensures that the data I’m using is reliable and well-governed. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a comprehensive and efficient platform for managing data workflows. We understand your concerns about cost management and the learning curve for beginners, and we will share your feedback with our team to further review. ### 25. Databricks Unifies Data, Analytics, and ML for Scalable Lakehouse Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Harshavarthini G. | Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** Databricks is especially helpful because it brings data engineering, analytics, and machine learning together in a single unified platform, which reduces the need to manage multiple separate tools. Built on Apache Spark, it can process massive datasets quickly and scale smoothly as workloads grow, making it a strong fit for big data use cases. It also supports collaborative notebooks where teams can work together in languages like Python and SQL, which makes it easier for data scientists and engineers to collaborate effectively. With its lakehouse architecture powered by Delta Lake, Databricks combines the flexibility of data lakes with the reliability of data warehouses, helping ensure better data consistency and performance. In addition, it integrates with tools like MLflow to streamline the machine learning lifecycle end to end, from experimentation through deployment. Overall, Databricks simplifies complex data workflows, improves performance, and helps organizations build scalable data and AI solutions more efficiently. **What do you dislike about Databricks?** Databricks does have some limitations, although many of them feel more like trade-offs than outright negatives. A frequently cited drawback is cost: while the platform is flexible and scalable, expenses can rise quickly if clusters aren’t managed carefully. At the same time, that cost often reflects its ability to handle very large workloads efficiently when it’s properly optimized. Another consideration is the learning curve, especially for beginners who aren’t familiar with Apache Spark or distributed systems. That complexity can be challenging at first, but it also comes with the benefit of powerful capabilities once you get comfortable with it. Some users also find that debugging and performance tuning are less straightforward than with simpler tools; however, Databricks offers detailed monitoring and optimization features that can make these tasks easier over time. Finally, because it’s a managed platform, there can be a sense of reduced control compared with fully self-managed systems. In return, it removes much of the operational burden that comes with infrastructure management. Overall, while these areas may be seen as the “least helpful” aspects, they’re often balanced by the platform’s scalability, integration, and productivity gains. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of fragmented data and disconnected workflows across multiple business verticals by providing a unified lakehouse platform. In my role as a data engineer, this allows me to consolidate data from different sources into a single, reliable system using Apache Spark for scalable processing and Delta Lake for ensuring data quality and consistency. This significantly reduces pipeline complexity, improves reliability, and enables faster delivery of clean, governed data to downstream teams. As a result, I’m able to support analytics and machine learning use cases more efficiently while minimizing operational overhead and improving overall productivity across the organization. **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Databricks. It's great to hear that the platform's ability to bring together data engineering, analytics, and machine learning in a single unified platform is benefiting your organization. We understand the trade-offs and challenges you've mentioned, and we're continuously working on these parts of our platform. ### 26. Databricks Streamlines End-to-End ETL with Unity Catalog and AI-Powered Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Dinesh Sundar S. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** What stands out to me is how Databricks simplifies the end-to-end ETL lifecycle. The platform’s steady integration of new features has noticeably reduced the friction of ingesting data from a wide range of source systems. Unity Catalog (UC) has also been a game-changer for data administration. It offers a centralized, robust governance layer that makes managing complex environments feel much more intuitive and easier to control. I’m especially impressed by the recent AI-driven updates. Genie Code has become an essential part of my workflow; it has dramatically improved my debugging speed and is already proving to be a valuable asset in my current UC migration project. Overall, the way Databricks blends traditional data engineering with assisted intelligence feels genuinely forward-thinking. **What do you dislike about Databricks?** While Auto Loader is powerful, there are still notable gaps in the Lakehouse Data Pipeline (LDP) around schema inference. Right now, when inferSchema is enabled, the inferred schema only applies to the first level of the hierarchy. In complex datasets with multi-nested fields, the lack of deep schema inference creates manual overhead and makes streaming CDC pipelines harder to build and maintain. Lakeflow Connect feels like a step in the right direction, but the library of native connectors still seems incomplete compared to some competitors. And while the AI features (like Genie) are promising and genuinely interesting, they still come across as being in a “developing” stage—sometimes lacking the consistency you need for high-stakes production environments. I’d like to see these capabilities evolve from “innovative extras” into hardened, production-ready tools. **What problems is Databricks solving and how is that benefiting you?** The Problem: Data Silos & Inefficient Support Operations In many organizations, critical institutional knowledge ends up scattered across disconnected systems such as MySQL (structured), Jira (transactional), and Confluence (unstructured). When information is fragmented this way, support teams struggle to find fast, accurate answers for incoming tickets. The result is higher MTTR (Mean Time to Resolution) and a lot of repetitive, manual effort. The Solution: A Unified “Intelligence Platform” Databricks addresses this by serving as a single fabric that connects these silos. In my work, I focus on using the Lakehouse Data Pipeline (LDP) to ingest and unify these different sources into one governed environment. How this benefits my project: I use Databricks for seamless ingestion, centralizing data from MySQL, Jira, and Confluence to build a comprehensive “Knowledge Base” without having to manage multiple, disparate ETL tools. I also rely on native AI integration. With Mosaic AI Vector Search, I can convert the unified data into embeddings directly within the platform, which lets me build an AI Automation Agent for our ticketing system. Finally, it supports automated solutioning. The agent can run vector matching on newly created tickets against the full historical knowledge base and then propose accurate, context-aware solutions to engineers right away. The Impact The biggest benefit for us is operational velocity. Databricks has shifted our data from a passive archive into an active, “intelligent” engine. It reduces time spent on manual research and helps us automate the first line of support, improving the accuracy of ticket resolutions while lowering the burden on our technical teams. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that our platform's end-to-end ETL lifecycle simplification and Unity Catalog have been game-changers for your data administration. We appreciate your feedback on the AI-driven updates and are thrilled to hear that Genie has improved your workflow. We're committed to continuously enhancing our platform to provide a forward-thinking experience for our users. ### 27. Fast, Seamless Databricks for Big Data Pipelines, and Analytics in One Place **Rating:** 4.5/5.0 stars **Reviewed by:** Demetrius A. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 31, 2026 **What do you like best about Databricks?** What I love most about Databricks is how fast and connected everything is. Compared to other platforms, it handles heavy big data pipelines without breaking a sweat. But the best part is how easy it is to use that data once it's processed. Whether I need to build a quick analytics dashboard or train custom machine learning models specific to our data, it all connects seamlessly. It just takes the headache out of moving data around and lets you do everything in one place. **What do you dislike about Databricks?** If I had to choose what I dislike, it mainly comes down to the cost and how complex it can be. First, it can get expensive very quickly. If you’re not careful about managing your computing clusters and shutting them down when you’re done, the bills can creep up on you. Second, it can sometimes feel like overkill for simpler tasks. Since it’s built for massive data, having to dig through complicated error logs when something breaks can be a real headache compared to using lighter tools. **What problems is Databricks solving and how is that benefiting you?** The main problem Databricks helps me solve in my business is performance. We used to wait for hours for pipelines to run in ADF, and now we can get them done in minutes. **Official Response from Janelle Glover:** > We're thrilled to hear that you find Databricks fast and seamless for handling big data pipelines and analytics. We understand your concerns about cost and complexity, and we are continuously working to optimize these aspects of our platform to address these challenges. ### 28. Unified ML Platform That Removes Infrastructure Friction **Rating:** 5.0/5.0 stars **Reviewed by:** Hirlekha M. | AI/ ML Technical Lead, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** The unified platform experience is genuinely hard to beat — having MLflow for experiment tracking, Unity Catalog for governance, vector search, and serverless endpoints all in one place removes so much infrastructure friction. Feature engineering pipelines and model deployment feel cohesive rather than stitched together. The SQL warehouse + notebook hybrid workflow also makes it easy to hand off between data engineering and ML work without context switching tools. **What do you dislike about Databricks?** Serverless endpoints have some sharp edges — Spark context initialization behaves differently than in interactive clusters, which can cause silent failures if you're not careful about where you initialize things. Cold start latency on serverless is also noticeable for low-traffic production endpoints. Documentation around some of the newer features (like vector search index configs) tends to lag behind the actual product behavior, so you end up doing a lot of trial and error. **What problems is Databricks solving and how is that benefiting you?** We use Databricks to consolidate ML model development, feature engineering, and deployment for a cards and payments platform — work that previously required juggling separate tools for data processing, training, and serving. The unified environment means our ML engineers can go from raw transaction data to a deployed churn prediction model without leaving the platform. MLflow tracking keeps experiments reproducible, and Unity Catalog gives us the data governance story our banking client needs. It's cut down a significant amount of the coordination overhead that comes with multi-tool ML pipelines. **Official Response from Janelle Glover:** > It's great to hear how Databricks has streamlined your ML workflows and reduced coordination overhead. We appreciate your feedback on the serverless endpoints and documentation, and we'll strive to address these issues to enhance your experience with our platform. ### 29. From 1 Hour to 10 Minutes: How Databricks Modernized Our Workflow **Rating:** 4.5/5.0 stars **Reviewed by:** Mukundan R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 30, 2026 **What do you like best about Databricks?** We used to use ADF to get data from SQL Server and then work on it in Databricks before putting it into Salesforce. The whole process took a time more than an hour because ADF added extra work. Now everything happens inside Databricks. We transform the raw data in Databricks and put in into Salesforce all in one place. This has made the whole process much faster, it now takes 10 minutes. That is an improvement from what we had with ADF. Delta Lake has also been really useful. It helps us keep track of changes and go back if something goes wrong. We can see what happened before . Fix mistakes easily. Delta Lake also makes sure the data is good before it goes into the pipeline. It stops data from getting in and causing problems later on in Salesforce. This makes the whole process more reliable and easier to take care of. **What do you dislike about Databricks?** Databricks is really good at what it does.. Sometimes it takes a while to get the cluster up and running.. The user interface is slow at sometimes. This can be annoying when we are in a hurry to get things done for Salesforce. The Salesforce connectors in Databricks can be a bit tricky to work with. They often need to be set up right and do not work as we expect. This means we have to put in work when we are trying to figure out problems or keep an eye on the pipelines, in Databricks for Salesforce. **What problems is Databricks solving and how is that benefiting you?** It is solving our performance and reliability issues - by allowing us to extract, transform and load the data into Salesforce all in one place without ADF. This unified workflow has reduced our runtime from 1 hour to 10 minutes giving us faster job completion and on time Salesforce data updates.With delta lake features like ACID transactions and time travel,our data is more accurate and easier to recover when something goes wrong. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been able to significantly improve your workflow by reducing the runtime. It's great to know that Delta Lake has been useful in maintaining data accuracy and providing easier recovery options. We understand your concerns about the cluster setup time and the user interface speed, as well as the challenges with the Salesforce connectors. We appreciate your feedback and will share it with our team for further improvements. ### 30. Databricks: Feature-Rich, User-Friendly, and Keeps Everything in One Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Sivabalan A. | Technical Lead - Data Engineering, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Among the various platforms I’ve worked with, Databricks stands out as a genuinely cohesive environment. It feels less like a bundle of disconnected features and more like a unified workspace—one that can evolve alongside the teams using it. The interface is intuitive enough to lower the barrier to entry, while still delivering the depth and power needed for heavy-duty engineering. One of its biggest strengths is how it consolidates the data lifecycle. By bringing engineering, data science, and SQL analytics under one roof, it helps dissolve the silos that often lead to “data drift” and miscommunication between departments. In practice, it also simplifies the underlying infrastructure, replacing a dozen specialized (and sometimes conflicting) tools with a single, clearer source of truth. Beyond simply “keeping things clean,” the platform also shines when it comes to collaborative transparency. With notebooks and experiments shared in real time, the gap between an initial data idea and a production-ready model can be dramatically shortened. On top of that, its commitment to open standards like Delta Lake means you’re not boxed into a proprietary black box—you’re building on a foundation that aligns with the broader data community’s direction. Overall, it strikes a rare balance: a polished, user-friendly wrapper around some of the most powerful distributed computing engines available today. **What do you dislike about Databricks?** The “Big Task” Breakdown When Genie processes a large volume of data, it often ends up sending a huge amount of JSON back to the browser so it can render those tables and visualizations. Memory overload: Browsers (and especially Chrome) can be real memory hogs. If a Genie response includes a very large result set or a massive execution plan, RAM usage can spike quickly, which can lead to that familiar “Not Responding” hang. The “DOM” lag: Every row in a table and every line of code becomes an element the browser has to keep track of. As you scroll or type, the browser has to repaint thousands of these elements. When the task is too large, the browser’s main thread can get tied up rendering, and your typing starts to feel like it’s trailing behind by a few seconds. **What problems is Databricks solving and how is that benefiting you?** You’ve nailed the core reason Databricks is winning over so many data teams: they’re reducing the “integration tax.” In most companies, you can easily lose around 30% of your time just moving data between the “storage” tool, the “processing” tool, and the “BI” tool. The AI/BI Dashboard is a great example of this broader shift—from a “collection of tools” to a more unified platform. What began as a basic visualization layer has evolved into a “Compound AI” system. Here’s how it has become so useful: The “Ask Genie” integration: You’re no longer limited to staring at a static chart. As of 2026, every published dashboard includes an “Ask Genie” button by default. If a stakeholder notices a spike in a line chart, they don’t have to call you; they can right-click the chart and ask, “Genie, why did this drop on Tuesday?” and it will use Agent mode to track down the driver. Direct-to-warehouse speed: Because it lives inside Databricks, there’s no need to “extract” data to a separate BI server. It queries the data where it already lives (Unity Catalog), which means the dashboard stays as fresh as your last ETL run. AI-assisted authoring: You can build entire widgets just by describing what you want. Instead of dragging fields around, you can type, “Show me a funnel chart of our sales conversion by region,” and it generates the SQL and the visualization for you. Deep governance: Since it’s built in, your security policies (row-level security, tags) follow the data automatically. You don’t have to recreate permissions in a separate tool like Tableau or Power BI. **Official Response from Janelle Glover:** > Thank you for highlighting the benefits of Databricks in reducing the 'integration tax' and streamlining data movement between storage, processing, and BI tools. We're pleased to hear how the AI/BI Dashboard and Genie have been valuable in providing direct-to-warehouse speed and AI-assisted authoring. ### 31. Databricks Genie Code - Agentic Applied AI for end-end SDL liefecycle **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Senior Cloud Solution Architect - Accenture Data & AI (Applied Intelligence), Enterprise (> 1000 emp.) **Reviewed Date:** October 03, 2023 **What do you like best about Databricks?** Genie Code 1) Genie Code automated our ETL processes, reducing manual effort and increasing efficiency. With Agentic’s SDL, we implemented CI/CD pipelines for faster, seamless updates and deployments. 2) Genie Code streamlined complex STTM mappings, improving accuracy and speed. Agentic’s real-time updates ensured mapping adjustments were made dynamically to align with changing transaction data. 3) We defined automated unit tests using SKILL.md, ensuring data transformations are validated before deployment. This reduced errors and ensured data quality, boosting confidence in our analytics. 4) Using Skills.md, we added custom extensions to Genie Code, such as integrating third-party data for enriched reports. This agility allowed us to quickly adapt to business needs and deliver new capabilities. 5) Agentic’s SDL enabled real-time data processing, providing immediate analytics for decision-making. Our marketing and sales teams now act on fresh data instantly, improving response times and overall efficiency. **What do you dislike about Databricks?** Hope it can be improved in next update - Debugging issues in complex workflows can be time-consuming due to limited visibility into intermediate data transformations. Genie Code lacks advanced error recovery mechanisms, making it difficult to manage failures in large-scale data pipelines. As data volume increases, Genie Code’s performance can degrade, requiring significant manual adjustments to ensure smooth operation at scale. **What problems is Databricks solving and how is that benefiting you?** 1) Scalable Processing - Built on Databricks' Spark-based architecture, Genie Code efficiently handles and scales processing for massive datasets, ensuring performance even with increasing data volumes. 2) Genie Code automates end-to-end ETL workflows, from data extraction to transformation and loading, streamlining data operations and eliminating manual tasks. 3) Real time collaboration - Genie Code enables real-time collaboration across teams by using shared notebooks, making it easier for data professionals to build and refine workflows collectively. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks Data Intelligence Platform is helping you with unified lakehouse platform, workflow orchestration, integrations, and data sharing. We are committed to providing solutions that meet your business needs. ### 32. Databricks’ Unified Platform: Fast SQL, Streamlined Pipelines, and Context-Aware AI **Rating:** 5.0/5.0 stars **Reviewed by:** Deeraj R. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** The unified platform experience is what keeps me on Databricks. Having notebooks, pipelines, SQL warehouses, ML, and governance all in one place under Unity Catalog means I’m not constantly stitching together five different tools just to get work done. Lakeflow Pipelines (formerly DLT) makes it straightforward to build medallion-architecture pipelines, and the Photon engine delivers real performance gains on SQL workloads without requiring any code changes. Recent additions like Genie Code and background agents also show they’re serious about agentic AI—it doesn’t feel like a bolt-on copilot, because it can actually understand your data context through Unity Catalog. Serverless compute has been another big quality-of-life improvement as well, since I no longer have to wait for cluster spin-up when I just want to run quick, ad hoc queries. **What do you dislike about Databricks?** Cost management can be tricky—DBUs add up quickly if you’re not careful with cluster sizing and warehouse auto-scaling. The pricing model also isn’t always transparent, especially when you’re mixing serverless and classic compute. Unity Catalog is powerful, but the initial setup and the migration from legacy HMS can be painful, particularly for large orgs with years of existing Hive metastore objects. The documentation is generally good, yet it sometimes lags behind new feature releases. On top of that, the workspace UI can feel sluggish at times, especially when you’re working with a large number of assets. **What problems is Databricks solving and how is that benefiting you?** Before Databricks, our data stack was fragmented — separate tools for ETL, analytics, ML, and governance. That meant constant context-switching, duplicated data, and governance gaps. Databricks consolidates all of that into one lakehouse platform. Delta Lake gives us reliable ACID transactions on the data lake, Unity Catalog handles lineage and access control across the board, and SQL warehouses let our analysts self-serve without needing a separate data warehouse product. It's cut our pipeline development time significantly and made data governance something we can actually enforce consistently instead of hoping for the best. **Official Response from Janelle Glover:** > We're glad to hear that you're enjoying the unified platform experience and finding value in Lakeflow Pipelines, Photon engine, and Genie Code. We understand your concerns about cost management and transparency in pricing, as well as the challenges with initial setup and workspace UI. Your feedback is valuable and will be shared with our team for further improvements. ### 33. All-in-One Databricks Platform with Strong Governance, Fast Spark Performance, and Genie **Rating:** 5.0/5.0 stars **Reviewed by:** Sabareeswara S. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** The all-in-one platform eliminates tool sprawl. Unity Catalog gives you governance, lineage, and discoverability without bolting on a separate catalog. The notebook UI is clean and makes iterating on PySpark fast. Genie is the standout AI feature it turns curated tables into natural language interfaces for business users, and the SDK lets you configure it programmatically so it stays maintainable. DLT handles pipeline orchestration well. Performance on Spark workloads is solid, especially with Photon. Integrations with Airflow, S3, and the broader ecosystem are straightforward. For the ROI, consolidating what used to require multiple tools into one platform pays for itself in reduced complexity. **What do you dislike about Databricks?** Pricing can be hard to predict. Compute costs scale quickly if you're not careful with cluster sizing and SKU selection, and it's not always obvious which workload tier you actually need until you see the bill. The notebook IDE, while functional, still lags behind a real editor for refactoring, multi-file navigation, and code review workflows **What problems is Databricks solving and how is that benefiting you?** Tool consolidation is the biggest one. Before, you'd need separate systems for ingestion, transformation, warehousing, governance, and serving each with its own learning curve, maintenance overhead, and integration headaches. Databricks collapses that into a single platform. Unity Catalog solves the data governance problem by giving you lineage, access control, and discoverability in one place instead of managing permissions across disconnected systems. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that you find Delta Live Tables, Unity Catalog, and Genie beneficial. We also appreciate your feedback on the pricing and will share your comments with our team. ### 34. Databricks Keeps Removing Friction with Strong Governance and Intuitive AI Tools **Rating:** 4.5/5.0 stars **Reviewed by:** Praveenkumar S. | Solutions Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** What I like most about Databricks is how its features have consistently matched the evolving needs of engineering teams. Over the years, I’ve seen it grow from a solid data platform into a workspace that genuinely streamlines how we build and manage data and AI solutions. Unity Catalog has been one of the biggest improvements for us having a single place to manage permissions and lineage has removed a lot of manual steps we used to handle separately across systems. Genie AI and BI have also become part of my regular workflow; being able to generate SQL or explore datasets through natural conversations helps teams get to answers faster, especially when we’re under time pressure. The Apps capability has added unexpected value by letting us create and share simplified internal tools directly within the platform, eliminating the need to stand up extra infrastructure. And with Lakebase, we’ve been able to support more transactional-style use cases without losing the flexibility of a lake, which has made certain pipelines far easier to maintain. Altogether, these improvements have removed a lot of friction from day‑to‑day work and made the platform something I genuinely enjoy using as it continues to evolve. **What do you dislike about Databricks?** What I dislike about Databricks is that some of the newer AI experiences especially Genie for code generation can feel unstable at times and may lose context during longer development sessions. It disrupts my workflow when the assistant can’t retain earlier logic or maintain continuity across multiple iterations. I’ve also noticed a gap in native connectors for certain enterprise systems like DFS, SMB shares or windows-based source systems, and platforms such as DB2 on AS/400, which many customers still rely on. Even though Databricks continues to expand its ecosystem, the lack of direct connectivity in these areas often means we need extra middleware or custom pipelines to bridge the gap. None of these are deal-breakers, but they’re areas where the platform’s otherwise smooth experience can still feel a bit incomplete. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us address several long‑standing challenges in how we manage and deliver data and AI. Before adopting its newer capabilities, we were dealing with fragmented governance, duplicate datasets, and a lot of manual effort to keep permissions and lineage consistent across different systems. Unity Catalog improved this by giving us a single place to manage security and ownership, which reduced confusion across teams and noticeably cut down on rework during audits. We also used to spend a significant amount of time helping teams explore data or draft queries. With Genie AI and BI, they can now generate SQL, summaries, and visual insights more independently. As a result, the time from a question to a usable answer has shortened, especially when we’re working under tight delivery cycles. Another pain point was building small internal tools around our data. Setting up separate infrastructure or hosting environments created unnecessary overhead. With Databricks Apps, we can now build and share these tools within the platform itself, which saves setup time and reduces ongoing maintenance. Finally, we struggled to support workloads that needed both the flexibility of a lake and the reliability of a database. Lakebase helped close that gap by enabling transactional‑style operations directly on our lake data, which simplified several pipelines and reduced the number of systems we have to maintain. Overall, Databricks has moved us from juggling multiple disconnected tools to working in a more unified and predictable environment. That shift has sped up delivery, lowered operational overhead, and improved the clarity of our workflows. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear that our platform has been able to consistently meet the evolving needs of your engineering teams and streamline your data and AI solutions. We appreciate your feedback on Unity Catalog, Genie, and Lakebase, and we're committed to continually improving and evolving our platform to provide a smooth and enjoyable user experience. ### 35. Fast, Governed Self-Service Data Exploration with Databricks Genie **Rating:** 3.5/5.0 stars **Reviewed by:** Yuvashree M. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** As a data engineer, I use Databricks Genie to interact with data in natural language, while still relying on the same governed tables, metrics, and semantic models that my team has built. Instead of jumping straight into SQL notebooks for every exploratory ask, I or business users can phrase questions in plain language and let Genie translate them into structured, catalog‑aware queries. This keeps self‑service fast but also secure and governed. **What do you dislike about Databricks?** Laptop stability when multitasking My laptop can hang or become noticeably sluggish when I’m working with multiple Genie tabs and dashboards at the same time, especially during heavier queries or more demanding visualizations. This hurts the overall user experience and can slow down iterative development and analysis. Latency with complex data models With very wide schemas or more complex semantic models, Genie sometimes selects suboptimal joins or an overly broad/narrow level of granularity. As a result, I still need to review the generated SQL and optimize it myself. In that sense, it remains a helpful assistant rather than a fully autonomous query engine. **What problems is Databricks solving and how is that benefiting you?** In a recent project, the business wanted to understand a decline in customer‑lifetime‑value (CLV) in a specific region. A product manager used Genie to explore CLV trends by region and cohort, excluding refunds, directly from an AI/BI dashboard. From that conversation, I captured the core logic, wrapped it into a Delta Live Table pipeline, and scheduled it as a recurring job. This reduced ad‑hoc requests by roughly 30–40% and enabled ongoing self‑serve access to CLV insights while I focused on tuning performance and data‑quality rules. Overall, Genie helps me talk with my data in natural language, improves how quickly we uncover insights, and supports better data‑quality practices—though working across many Genie‑backed tabs can strain local hardware and sometimes slow down the workflow. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with using Genie for self-service data exploration. We apologize for the issues you've noticed with stability and latency. Our team is actively working to address these concerns and enhance the user experience. ### 36. Databricks Genie Nails Unity Catalog Migrations with Context-Aware Guidance **Rating:** 4.0/5.0 stars **Reviewed by:** Nandhini E. | Senior Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks Genie's contextual understanding of Unity Catalog is genuinely impressive. While working through a complex UC migration, navigating three-level namespaces, volume paths, security modes, and widget-driven SQL execution, Genie reasoned through the specifics instead of falling back on generic answers. It really speaks the UC migration language, which cuts down on a lot of back-and-forth and makes troubleshooting feel more direct. Overall, the platform is powerful for managing large-scale data engineering work across Python, Scala, and notebook-based pipelines, all in one place. **What do you dislike about Databricks?** My biggest frustration with Genie is the lack of persistent session memory. On a long-running migration project with 60+ test cases and multiple interconnected components, having to re-establish context every session creates real overhead. Genie also struggles with cross-component reasoning: it handles individual notebooks well, but tracing issues across multiple layers of a framework is still largely a manual effort. Occasionally, the responses feel overly cautious when what’s needed is a more direct, confident answer. **What problems is Databricks solving and how is that benefiting you?** We’re using Databricks to carry out a full Unity Catalog migration for a large, automated ingestion framework, moving off the legacy Hive Metastore while also upgrading the runtime environment. Databricks provides a unified platform where the migration work, testing, and validation can all happen in one place. During testing, Genie in particular helped speed up root-cause analysis, for example, it pinpointed why a data extraction notebook was failing to resolve UC-managed table references and identified that adding a USE CATALOG statement was the fix. That kind of targeted, context-aware assistance directly reduces investigation time during complex migrations. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping to streamline your Unity Catalog migration and testing processes. We appreciate your specific example of how Genie's context-aware assistance has directly reduced investigation time during complex migrations. We also appreciate your feedback on efficiency and will take your comments into consideration for future improvements. ### 37. Driving AI and Data Innovation with a Unified Databricks Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay Kumar P. | Associate Consultant-Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** August 09, 2023 **What do you like best about Databricks?** I use Databricks for ETL, Reporting, and AI, and I appreciate that it works as one unified solution for all data and AI needs. It makes it easier to track data and create insights, helping us deal with data silos. I like the Unity Catalog as it helps us manage and govern data in one place. I also like using AgentBricks as a multi-agent system for creating AI applications from PDFs and other documents. I find Genie valuable as it allows business users to ask questions in natural language and get exact answers. The initial setup of Databricks was very easy, making the transition smooth. **What do you dislike about Databricks?** I think workflow could be improved by adding multiple triggers to the same pipeline, as for now, if we want to schedule the same pipeline multiple times in a day, we have to clone it for each time. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to eliminate data silos and make data tracking and insights creation easy. Unity Catalog manages data governance, AgentBricks develops AI applications, and Genie provides answers using natural language on structured data. **Official Response from Aunalisa Arellano:** > We're thrilled to hear that Databricks Intelligence Platform is providing value by addressing data governance issues and streamlining data management. Your feedback on the need for more robust workflows is noted, and we are committed to continuously improving our platform to better meet the needs of Data Engineers, ML Engineers, and Analysts. ### 38. Genie Code Agent Mode Made Our Migration to Databricks Fast and Accurate **Rating:** 4.0/5.0 stars **Reviewed by:** Dharun T. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** Genie Code (Databricks Assistant Agent) — I’m currently working on migrating existing workloads from ADF and SQLMI to Databricks. As part of that, I need to convert stored procedures and ADF dataflows into Databricks notebooks. Initially, we refactored all the code manually, but once Agent Mode was available in preview, we tried using it to convert the stored procedures and dataflows into Databricks PySpark code. I was impressed by the accuracy: it handled about 90% of the code conversion without errors, aside from some case-handling and similar adjustments. Also, Lakeflow Connect helped me connect SharePoint and SFTP data to Databricks more easily. **What do you dislike about Databricks?** It’s not a major issue, but in my project the client asked us to generate table and column descriptions using AI in Unity Catalog. For each environment, these descriptions vary, and I have around 300 tables just in the Bronze zone. Having to click into each table and generate AI descriptions one by one is very time-consuming, and the results are not consistent across environments. It would be much more efficient if we had an option to generate descriptions at the schema level, and if there were an information schema or system tables that stored table and column descriptions as metadata. That way, we could easily replicate them across environments. In some cases, clients also have source system documentation we could leverage to generate more accurate table and column descriptions. **What problems is Databricks solving and how is that benefiting you?** One of my main scenarios was migrating all the existing stored procedures and ADF dataflows into Databricks notebooks. Doing this manually took more than 6 hours to complete both the development and the validation. Later, we used Agent Mode Preview and converted over 80+ medium/complex stored procedures and 20+ ADF dataflows into Databricks notebooks. This saved more than 100+ hours, and it also generated validation scripts for each table to close out unit testing. Apart from the Agent Assistant, we also used external volume. Previously, we relied on the Azure library for file processing in ADLS storage, but we ran into rate-limit issues, couldn’t process in parallel, and sometimes the job would abort. After we created an external volume pointing to the required ADLS container, we achieved parallel processing and faster reads and writes, instead of using custom Python code. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Genie and Lakeflow Connect in Databricks! We're glad to hear that it has made your migration process fast and accurate. ### 39. Databricks: A True Unified Analytics & AI Platform That Boosts Speed and Reliability **Rating:** 5.0/5.0 stars **Reviewed by:** Amit D. | Data Architect, Enterprise (> 1000 emp.) **Reviewed Date:** March 26, 2026 **What do you like best about Databricks?** What I like best about Databricks is how it finally delivered what every data engineer/data professional has been wishing for — a true unified analytics and AI platform. I remember working across five different tools just to get a single pipeline from ingestion to reporting. Databricks collapsed all of that into one environment, and that changed everything for me. Delta Lake was the first breakthrough. When it arrived around 2020, ACID transactions and time‑travel immediately eliminated the operational pain we used to consider “normal.” If a job corrupted a table, I could roll back to a previous version in seconds instead of spending hours restoring backups. That reliability alone saved multiple downstream failures. Before Delta existed, our pipelines relied heavily on overwrite patterns because there was no reliable way to apply updates or handle late‑arriving data safely. Overwrites were slow, expensive, and risky — especially for large tables. A single failure during overwrite could leave the table in a half-written, inconsistent state. Processing took longer, compute costs shot up, and recovery often meant manually rebuilding partitions from scratch. The ROI became obvious as soon as we used Databricks end‑to‑end. Because one platform handles ingestion → transformation → ML → BI → governance, we retired entire categories of legacy tools and reduced operational overhead dramatically. Then Genie arrived — and it genuinely transformed my day‑to‑day work. I once needed a PySpark module for data quality checks. Genie generated the full logic — null checks, schema validation, aggregations — in seconds. Instead of spending 30 minutes writing boilerplate, I spent 3 minutes refining the logic. It shifted my focus from syntax to decisions. Integrations are another strength. Connecting Databricks to S3, SQL Server, and especially Power BI has been seamless. Publishing Delta tables directly to BI models removed the need for brittle extracts and sped up refreshes. Unity Catalog made everything even cleaner with consistent permissions and lineage. Performance is consistently strong when it matters — heavy joins, window functions, multi‑stage pipelines, or streaming workloads. Serverless compute starts instantly, and workloads scale predictably even under pressure. Finally, onboarding surprised me. Features like serverless compute, natural‑language queries, AI‑generated code suggestions, and automatic comments make Databricks intuitive even for engineers new to Spark. It feels like the platform actively helps you learn. In short: Databricks lets me work faster, recover instantly, integrate seamlessly, and scale confidently — all in one place. It’s the rare platform that improves both speed and reliability at the same time. **What do you dislike about Databricks?** What I dislike most about Databricks is the cost visibility and predictability. Even as an experienced engineer, it can be difficult to get a straight, real‑time view of what a workflow will cost before running it. Photon vs. standard runtime, autoscaling behaviour, shuffle-heavy operations, DBUs—these can stack up quickly, and cost surprises happen unless you actively monitor and tune everything. A simple pipeline misconfiguration can quietly double your spend. Another challenge is the rapid pace of new features and changes. Databricks innovates incredibly fast, which is great, but it also means features may land before documentation, best practices, or governance patterns are fully mature. Sometimes functionality behaves differently across runtimes or cloud providers, and staying on top of everything requires continuous learning and refactoring. This can create team friction and technical debt. In short: Databricks is exceptional, but the cost model isn’t always transparent, and the rapid feature rollout can introduce operational complexity that teams must actively manage. **What problems is Databricks solving and how is that benefiting you?** Business : Before adopting Databricks, our aerospace analytics environment — particularly around Customer engine health monitoring — suffered from the same challenges many traditional engineering organisations face. We had multiple disconnected systems handling telemetry ingestion, fault-code processing, fleet analytics, and maintenance prediction. Data from engine sensors (FADEC, vibration, thermals, oil systems) arrived in different formats and needed heavy manual work just to normalise. Pipelines relied on full overwrites because our legacy setup didn’t support updates or late-arriving data, which made processing slow and expensive. We struggled with slow ingestion of engine telemetry, inconsistent datasets across engineering teams, and long turnaround times for anomaly detection models. Architecture challenge: Before using Databricks, we were operating in a fragmented data landscape. We had multiple systems, disconnected storage layers, and a heavy reliance on overwrite‑based ETL jobs because our old data platform couldn’t support updates, late‑arriving data, or ACID guarantees. This meant pipelines were slow, error‑prone, and expensive. Rolling back bad data could take hours, and data inconsistencies across teams were common. We struggled with siloed systems, slow pipelines, unreliable data, and high operational cost. We struggled with manual overwrites and inconsistent data — but now we can use Delta Lake with ACID and time‑travel, which has resulted in: Instant rollback from data corruption scenarios Reliable incremental processing instead of full overwrites Consistent data consumed across engineering, BI, and ML teams This reduced our telemetry pipeline processing window from hours to under 30 minutes for a fleet‑wide daily batch.. We struggled with multiple tools and duplicated architectures — but now we have one unified Lakehouse, which has resulted in: A single platform for ingestion → transformation → ML → BI → governance Removal of 3–5 legacy tools (ETL schedulers, BI extracts, legacy ML infra) Lower maintenance and licensing overhead We struggled with slow development cycles — but now we can leverage Genie for AI‑assisted engineering, which has resulted in: 70–80% faster creation of PySpark modules Automatic generation of schema checks, null checks, and DQ logic More time spent on decisions, less on boilerplate code For example, a data quality module that used to take 30 minutes now takes 2–3 minutes to scaffold. We struggled with inconsistent governance — but now Unity Catalog gives us end‑to‑end visibility, which has resulted in: Faster onboarding (reduced from days to minutes) Centralised permissions, lineage, and audit trails Stronger compliance alignment We struggled to scale pipelines and ML workloads — but now we use distributed compute + Photon, which has resulted in: Large joins and window operations executing up to 10× faster Stable handling of terabyte‑scale datasets Predictable performance even under heavy workloads **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're thrilled to hear how our platform has improved your workflow and provided reliability and speed. We appreciate your feedback and are committed to continuously enhancing our platform to better serve your needs. ### 40. Reimagining Data Workflows & Insights with Genie: NLQ spaces, Agent Mode, and Intelligent Coding **Rating:** 4.5/5.0 stars **Reviewed by:** Senthil K. | Associate Director, Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2026 **What do you like best about Databricks?** 1) In our implementation, Genie Space is actively used to enable NLQ-based access across multiple data products like Finance, HR, Marketing, Sales, and Supply Chain (inventory, demand planning, and replenishment), reducing dependency on data teams for ad-hoc queries. 2) We designed separate Genie Spaces for each BU/team/data product, ensuring domain-level isolation while still supporting cross-functional querying where required (e.g., Finance + Sales joins). Each Genie Space is carefully configured with curated data tables, business-level instructions, and semantic context, which significantly improves the accuracy of SQL generation. 3) We provide few-shot examples, guided prompts, and sample business questions tailored to each domain, helping Genie understand real business intent instead of generic query patterns. 4) In Chat Mode, business users directly ask questions in natural language, and Genie translates them into SQL and returns results, which has improved self-service analytics adoption. 5) In Agent Mode, Genie goes beyond SQL generation by creating a logical execution plan, breaking down complex queries into multiple steps before querying the underlying data. 6) We built a dedicated Anomaly Detector Genie Space, where users ask questions about cluster cost, performance issues, and inefficient workloads. This anomaly-focused Genie analyzes long-running jobs, inefficient queries, and cluster utilization patterns, using historical workload data to identify optimization opportunities. 7) A key implementation is notebook-level analysis, where Genie highlights code issues, shows before vs after optimization, categorizes problems (performance, cost, inefficiency), and explains improvements clearly. 8) Genie also provides quantified recommendations, including expected cost savings (e.g., idle cluster reduction, query tuning impact) and workload-based optimization strategies, making it highly actionable for engineering teams. 9) We extended Genie into Genie Code integrated with Databricks AI Assistant, enabling an agentic development experience directly within our data engineering workflows. Our team defined custom skills in Markdown (MD files) such as Coder, Tester, Mapper, and Data Generator, which are attached to Genie Code to modularize capabilities. These skills are used to support end-to-end SDLC activities, including code generation, transformation logic creation, test case design, and synthetic data generation. 10) Genie Code operates by first creating a structured execution plan, outlining all required steps before starting any development activity. It then breaks the plan into a detailed to-do list, executing each step sequentially (e.g., create notebook → write transformation → validate logic → optimize code). 11) During execution, Genie Code follows a human-in-the-loop model, asking for approvals at every step with options like allow once, always allow, or read-only execution. The behavior of Genie Code is controlled through project-specific guidelines and instructions, ensuring it aligns with our coding standards, architecture patterns, and governance rules. 12) It acts as a co-developer within the workspace, assisting engineers in writing optimized code, validating logic, and ensuring best practices are followed consistently. We are leveraging it for proactive development workflows, where Genie not only executes tasks but also suggests improvements and optimization opportunities during development itself. This approach has enabled a “vibe coding” style of development, where engineers focus on intent while Genie handles structured execution, resulting in faster delivery, reduced manual effort, and improved overall code quality. **What do you dislike about Databricks?** Context limitation across Genie Spaces, also number of tables can be attached is 30 if i remember Agent Mode reasoning depth is good but not fully autonomous Need improvements in performance efficiency and reduce the latency **What problems is Databricks solving and how is that benefiting you?** 1) Bridging business and data teams through NLQ Databricks Genie solves the gap between business users and technical teams by enabling natural language access to data, reducing dependency on data engineers for everyday queries. 2) Eliminating data silos across domains By integrating data from Finance, HR, Sales, and Supply Chain, it helps us analyze cross-domain datasets, improving decision-making for use cases like demand planning and inventory optimization. 3) Accelerating self-service analytics With Genie Chat Mode converting NLQ to SQL, business users can independently fetch insights, significantly reducing turnaround time for reporting and analysis. 4) Handling complex analytical queries with Agent Mode Genie Agent Mode solves complex query scenarios by breaking them into structured execution plans, which is especially useful for multi-step analytical and optimization problems. 5) Improving cost and performance visibility Through our Anomaly Detector Genie Space, Databricks helps identify cluster inefficiencies, long-running jobs, and costly queries, giving clear visibility into platform usage. 6) Driving workload optimization and cost savings The platform provides actionable recommendations like query tuning, cluster right-sizing, and idle resource reduction, helping us optimize cost based on actual workload patterns. 7) Enhancing code quality through notebook analysis Genie analyzes notebook code and highlights performance issues with before/after comparisons, enabling developers to improve efficiency and follow best practices. 8) Supporting proactive development with Genie Code Databricks enables an agentic development workflow, where Genie Code assists in planning, coding, testing, and executing tasks step-by-step, reducing manual effort. 9) Standardizing development using skill-based automation By attaching custom skills (Coder, Tester, Mapper, Data Generator), we ensure consistent development practices and faster onboarding for new use cases. 10) Increasing overall productivity and faster delivery Combining Genie Space and Genie Code, Databricks significantly improves developer productivity, reduces iteration cycles, and accelerates delivery of data solutions, while maintaining governance and control. **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Genie, including its ability to bridge the gap between business and data teams, eliminate data silos, and improve cost and performance visibility. We understand your concerns about the limitations of Agent Mode and the need for further autonomy. We will work on addressing these areas to enhance your overall experience. ### 41. A Unified Platform for Scalable Data & AI Workloads **Rating:** 4.5/5.0 stars **Reviewed by:** Janani D. | Senior Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** Databricks is great because it brings everything you need for data and AI into one place. Instead of switching between different tools for data engineering, data cleaning, analytics, and machine learning, you can do it all in a single environment. That makes life a lot easier. **What do you dislike about Databricks?** Databricks is not beginner-friendly. You often need solid data engineering skills to use it effectively. Reviews point out that while Databricks is extremely capable, it’s “a high‑end workshop” that requires expertise and is not easy for less technical teams.Databricks uses cost units (DBUs), which many people find difficult to estimate and manage. Even expert reviews highlight that its pricing is famously complicated and can hide unexpected costs. **What problems is Databricks solving and how is that benefiting you?** Databricks uses the Lakehouse architecture to combine the strengths of data lakes and data warehouses into one unified platform. This means structured and unstructured data live together and are ready for analytics or machine learning. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks' architecture is benefiting you. We designed our platform to address the challenges of managing structured and unstructured data, and it's great to hear that it's making a positive impact on your analytics and machine learning workflows. ### 42. Databricks Notebooks Make Collaboration Seamless Across Python, SQL, and Scala **Rating:** 5.0/5.0 stars **Reviewed by:** Joseph F. | Cloud Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 25, 2026 **What do you like best about Databricks?** Databricks collaborative notebooks are really useful and let me work in whatever language I need to meet my requirements effectively. The ability to mix Python, SQL and even Scala within a dashboard makes collaboration and teamwork much smoothet. I also appreciate how easily it integrates with other tools and cloud platforms, so it fits into my existing workflows without very little friction. **What do you dislike about Databricks?** I like their customer support and the frequent updates are a big reason this has become my favorite for data management, I also appreciate how well it integrates with external tools like Power BI for reporting its really good. **What problems is Databricks solving and how is that benefiting you?** Its simplifies cross team collaboration and helps us work through large datasets without having to worry too much about infrastructure or analytics overhead. Calcuations and reporting are fast, which has improved our development cycles and reduced the back and forth between the engineering and analytics teams. **Official Response from Janelle Glover:** > It's great to hear that Databricks is simplifying cross-team collaboration and improving development cycles for you. We strive to provide a platform that reduces infrastructure and analytics overhead, allowing teams to focus on their core objectives. ### 43. Centralized Dashboard with Smooth, Cost-Saving Autoscaling **Rating:** 4.5/5.0 stars **Reviewed by:** Kimberly G. | Software Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** March 29, 2026 **What do you like best about Databricks?** Everything is centralized is a single dashboard spark jobs, notebooks and data pipelines. Autoscaling and auto termination genuinely help keep costs under control, and we could was a pleasant surprise that both run smoothly without any noticable lag. Sharing notebooks with the team is straightforward and cuts down on alot of back and forth. **What do you dislike about Databricks?** Finding older queries is really paunful. Anything beyond a few weeks becomes hard to track down, which makes it difficult to keep my data to day work flowing smoothly and to continue working without constant interruptions. **What problems is Databricks solving and how is that benefiting you?** We run ETL and ML workloads without having to worry too much about the underlying infrastructure. I can also manage inventory information, at least to some extent, without opening a bunch of different tabs. I spend less time troubleshooting clusters and more time actually working with the data. **Official Response from Janelle Glover:** > It's fantastic to hear that Databricks is helping you run ETL and ML workloads seamlessly, allowing you to focus more on working with the data and less on managing infrastructure. We're thrilled to be a part of your success. ### 44. All-in-One Platform for Data Engineering, ML, AI, and Data Management **Rating:** 4.5/5.0 stars **Reviewed by:** Akanksh M. | Machine Learning Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** It brings all the tech stacks together in one platform—data engineering, machine learning, AI, and data management—so everything is in one place. It also includes advanced features that make the platform feel complete and capable. **What do you dislike about Databricks?** We need more open-source, direct connectors to both legacy and current-generation platforms to enable better data extraction. These connectors should support real-time extraction as well as real-time data rendering. **What problems is Databricks solving and how is that benefiting you?** It brings all types of data into one place, which makes data and access management easier. I can build data warehouses and then downstream the data to AI BI dashboards and ML models, which is very useful. Special features like the feature store, serving endpoints, AI BI dashboard, and Genie help me understand the data, work with it more effectively, and ultimately reach my goals. **Official Response from Janelle Glover:** > Thank you for sharing what you like best about Databricks. We're glad that you're enjoying the feature store, AI BI dashboard, and Genie. We understand the importance of open-source connectors and real-time extraction, and we are continuously working to enhance our platform to better meet your needs. ### 45. Databricks: Powering Data and AI on One Platform **Rating:** 4.5/5.0 stars **Reviewed by:** Ajay P. | Associate Manager - Data Engineer, Small-Business (50 or fewer emp.) **Reviewed Date:** September 08, 2024 **What do you like best about Databricks?** The best part of databricks data intelligence is that it's very simple to use and have lot of fetures that helps us develope data pipeline and AI, and it help us us to easy implemet GenAI mostly RAG in production. LakeFlow made inetegration very esy with different sources as low code no code approch. **What do you dislike about Databricks?** Currently I think we don't have such dislike things in databricks as it's enabling new feature on daily basis and it's helping developers and analyist most. **What problems is Databricks solving and how is that benefiting you?** Problems: There is a complex land of the data to be secure and it needs security, privacy & governance over variety of sources that lead into error prone system. Solution: Databricks provides security features for data governance, access controls and compliance to secure the data using Unity Catalog. **Official Response from Janelle Glover:** > We're thrilled to hear that you find Databricks Data Intelligence Platform simple to use and packed with features for developing data pipelines and AI. It's great to know that it has been helpful in implementing GenAI and integrating with different sources through LakeFlow. ### 46. Unified Analytics Powerhouse with Minor Hiccups **Rating:** 4.0/5.0 stars **Reviewed by:** mohammad Gufran j. | Senior Associate Engineer (Azure Platform and Databrick Engineer), Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** April 25, 2026 **What do you like best about Databricks?** I like most about Databricks is that it brings data engineering, analytics, and AI workflow into one shared platform, which makes collaboration much easier. It's valuable for working with a large dataset and notebooks, and it helps set up suitable pipelines without the hassle of managing too many separate tools. **What do you dislike about Databricks?** Cost visibility and resource users can be hard to track, especially as more teams cluster and jobs start using the platform. I also like to sync up permission management. Clear troubleshooting for a job failure and a smoother experience around the workspace governance and configuration. CDC lake flow is always stuck for a last table and not giving a clear picture till now. Serverless logs are sometimes very difficult to track, making it hard to understand the reason for job failures. **What problems is Databricks solving and how is that benefiting you?** I find Databricks solves handling large-scale data processing and analytics by unifying data engineering, analytics, and AI workflow into one platform. It simplifies collaboration on notebooks and automation workflows, enabling faster work with big datasets using Spark. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks valuable for unifying data engineering, analytics, and AI workflow into one platform, making collaboration easier and simplifying big data processing. ### 47. Unified Platform with Powerful Features, Needs Faster Cluster Startups **Rating:** 4.0/5.0 stars **Reviewed by:** Yash P. | Software Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** April 23, 2026 **What do you like best about Databricks?** I appreciate how Databricks brought everything onto one unified platform, allowing our teams to collaborate in shared notebooks and ensuring data consistency with Delta Lake's ACID transactions. My favorite feature is Auto Loader, which automatically ingests new data files as they land in cloud storage, saving our team 2-3 hours a week on manual pipeline monitoring. Unity Catalog has been a game changer for us, providing a central place for governance and access control, which before was a mess. The initial setup was straightforward, and we had our first cluster and notebooks connected to S3 within a day, which was impressive given the platform's power. The workspace configuration and cloud integration guides are solid to follow. **What do you dislike about Databricks?** The cluster startup time is something that still catches us off guard. Cold start can take anywhere from 3-5 minutes, which gets frustrating when you are in the middle of an iterative debugging session and just need to test a quick fix. The cost management also needs some upgrades as currently the billing dashboards are improving but it still takes some digging to pinpoint exactly which job or user is driving up spend. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to unify our data processing and machine learning, reducing pipeline delivery delays by 40%. It enables team collaboration with consistent data, saving hours with the autoloader, and simplifies governance with Unity Catalog. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the unified platform and powerful features of Databricks, such as the Auto Loader and Unity Catalog. We understand your frustration with the cluster startup time and cost management, and we are continuously working to improve these aspects to provide a better user experience. ### 48. Streamlined Data Processing with Unmatched Speed **Rating:** 5.0/5.0 stars **Reviewed by:** Antarix K. | AI Architect, Mid-Market (51-1000 emp.) **Reviewed Date:** April 22, 2026 **What do you like best about Databricks?** I use Databricks for real-time data ingestion and processing as well as batch processing. I find it easy to use with PySpark, and I appreciate that it serves as a single platform for both real-time and batch processing. The in-memory processing drastically reduces processing time, and working with dataframes makes handling structured data straightforward. I like the fast execution and the ability to clean, massage, and manipulate data all on the same platform. It's also easy to deploy, and I enjoy the smooth CI pipeline with just one click. The initial setup was quite easy, and the product support made it a cakewalk. **What do you dislike about Databricks?** Databricks should come up with agentic framework integrated, making it a single stop for Data and AI. **What problems is Databricks solving and how is that benefiting you?** Databricks offers an easy-to-use platform for both realtime and batch processing. It integrates easily with PySpark and supports in-memory processing, significantly reducing processing time. Dataframes make handling structured data simpler. **Official Response from Jess Darnell:** > We're delighted to hear that Databricks has made real-time and batch processing easier for you, and that it has significantly reduced processing time. We're committed to providing a seamless experience and will continue to work on integrating new features to benefit our users. ### 49. Unified Data Engineering, Analytics, and ML on a Scalable Databricks Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Syed F. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** March 27, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it brings data engineering, analytics, and machine learning together in one platform. It streamlines the entire data pipeline—from ingestion and transformation through to serving—so I don’t have to rely on multiple separate tools to get end-to-end workflows done. Its integration with Spark and Delta Lake is another big plus, making it both scalable and dependable when working with large datasets. **What do you dislike about Databricks?** One challenge with Databricks is cost management and visibility. Since compute is abstracted through clusters and jobs, it can sometimes be difficult to track and optimize costs without additional monitoring or governance in place. **What problems is Databricks solving and how is that benefiting you?** Solves the problem of fragmented data ecosystems, where data engineering, analytics, and machine learning are handled in separate tools. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks valuable for data engineering, analytics, and machine learning. Thanks for sharing your feedback! ### 50. Unified Data Platform, Minor Cost and Complexity Challenges **Rating:** 4.5/5.0 stars **Reviewed by:** Abiola O. | DevOps Engineer **Reviewed Date:** April 16, 2026 **What do you like best about Databricks?** I like that Databricks provides a unified platform for data engineering and data science, eliminating friction across teams and enhancing the ability to accelerate development and deployments. It works especially well for end-to-end CICD pipelines. **What do you dislike about Databricks?** Well, in terms of what can be improved, I think, perhaps the cost management. If this can be looked into to make it more cost efficient for users, it will go a long way. And in addition to that, operational complexity sometimes presents a complex platform for new users to navigate easily. So if this can be addressed, then I think it should be a lot easier for engineers to work with. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for scalable workflows across multi-cloud environments, solving data silo unification and minimizing bottlenecks in complex data processing. It optimizes cost and governance while providing a collaborative workspace, real time data ingestion, and enhanced system reliability and performance. **Official Response from Jess Darnell:** > It's great to hear that Databricks is helping you with scalable workflows, data unification, and minimizing bottlenecks in complex data processing. We appreciate your insights on the benefits it provides. ## Databricks Discussions - [What is Lakehouse in Databricks?](https://www.g2.com/discussions/what-is-lakehouse-in-databricks) - 4 comments, 2 upvotes - [What are the features of Databricks?](https://www.g2.com/discussions/what-are-the-features-of-databricks) - 4 comments, 2 upvotes - [What does Databricks software do?](https://www.g2.com/discussions/what-does-databricks-software-do) - 3 comments - [What is Databricks unified analytics platform?](https://www.g2.com/discussions/what-is-databricks-unified-analytics-platform) - 3 comments - [View Databricks pricing details and edition comparison](https://www.g2.com/products/databricks/reviews?section=pricing&secure%5Bexpires_at%5D=2026-05-13+14%3A42%3A19+-0500&secure%5Bsession_id%5D=3ccf5fd5-f415-459f-b62d-df259ca83794&secure%5Btoken%5D=1b33860e8b92df90d45e728657275502a38f7d34bd6b0a1ac23ea1114302c0b8&format=llm_user) ## Databricks Integrations - [Agentforce 360 Platform (formerly Salesforce Platform)](https://www.g2.com/products/agentforce-360-platform-formerly-salesforce-platform/reviews) - 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[Azure Data Factory](https://www.g2.com/products/azure-data-factory/reviews) - [Azure Data Lake Store](https://www.g2.com/products/azure-data-lake-store/reviews) - [Azure Functions](https://www.g2.com/products/azure-functions/reviews) - [Azure Logic Apps](https://www.g2.com/products/azure-logic-apps/reviews) - [Azure OpenAI Service](https://www.g2.com/products/azure-openai-service/reviews) - [Azure Pipelines](https://www.g2.com/products/azure-pipelines/reviews) - [Azure Portal](https://www.g2.com/products/azure-portal/reviews) - [Azure SQL Database](https://www.g2.com/products/azure-sql-database/reviews) - [Claude Code](https://www.g2.com/products/anthropic-claude-code/reviews) - [Confluent](https://www.g2.com/products/confluent/reviews) - [Customer.io](https://www.g2.com/products/customer-io/reviews) - [Dash](https://www.g2.com/products/dash-for-brands-ltd-dash/reviews) - [data.world](https://www.g2.com/products/data-world/reviews) - [DAT iQ](https://www.g2.com/products/dat-iq/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [DigitalOcean](https://www.g2.com/products/digitalocean/reviews) - [Fivetran](https://www.g2.com/products/fivetran/reviews) - [GEN TDS](https://www.g2.com/products/gen-tds/reviews) - [Git](https://www.g2.com/products/git/reviews) - [GitHub](https://www.g2.com/products/github/reviews) - [Google Analytics](https://www.g2.com/products/google-analytics/reviews) - [Google Cloud BigQuery](https://www.g2.com/products/google-cloud-bigquery/reviews) - [Google Cloud Console](https://www.g2.com/products/google-cloud-console/reviews) - [Google Cloud Run](https://www.g2.com/products/google-cloud-run/reviews) - [HubSpot Marketing Hub](https://www.g2.com/products/hubspot-marketing-hub/reviews) - [Immuta](https://www.g2.com/products/immuta/reviews) - [Informatica Data Quality](https://www.g2.com/products/informatica-informatica-data-quality/reviews) - [JD Edwards World](https://www.g2.com/products/jd-edwards-world/reviews) - [Microsoft Copilot Studio](https://www.g2.com/products/microsoft-microsoft-copilot-studio/reviews) - [Microsoft Fabric](https://www.g2.com/products/microsoft-fabric/reviews) - [Microsoft Power Apps](https://www.g2.com/products/microsoft-power-apps/reviews) - [Microsoft Power Automate](https://www.g2.com/products/microsoft-power-automate/reviews) - [Microsoft Power BI](https://www.g2.com/products/microsoft-microsoft-power-bi/reviews) - [Microsoft SharePoint](https://www.g2.com/products/microsoft-sharepoint/reviews) - [Microsoft SQL Server](https://www.g2.com/products/microsoft-sql-server/reviews) - [Microsoft Teams](https://www.g2.com/products/microsoft-teams/reviews) - [Monte Carlo](https://www.g2.com/products/monte-carlo/reviews) - [MySQL](https://www.g2.com/products/mysql/reviews) - [ObjectWay SpA](https://www.g2.com/products/objectway-spa/reviews) - [Oracle Database](https://www.g2.com/products/oracle-database/reviews) - [Pega Platform](https://www.g2.com/products/pega-platform/reviews) - [PostgreSQL](https://www.g2.com/products/postgresql/reviews) - [PowerBI Portal](https://www.g2.com/products/powerbi-portal/reviews) - [Qualtrics Customer Experience](https://www.g2.com/products/qualtrics-customer-experience/reviews) - [React Native](https://www.g2.com/products/react-native/reviews) - [Salesforce Agentforce](https://www.g2.com/products/salesforce-agentforce/reviews) - [SAP Ariba](https://www.g2.com/products/sap-ariba/reviews) - [SAP ECC](https://www.g2.com/products/sap-ecc/reviews) - [SAS Viya](https://www.g2.com/products/sas-sas-viya/reviews) - [Seamless (formally Seamless.AI)](https://www.g2.com/products/seamless-formally-seamless-ai/reviews) - [ServiceNow IT Service Management](https://www.g2.com/products/servicenow-it-service-management/reviews) - [Sigma](https://www.g2.com/products/sigma-computing-sigma/reviews) - [Sisense](https://www.g2.com/products/sisense/reviews) - [SnapLogic Intelligent Integration Platform (IIP)](https://www.g2.com/products/snaplogic-intelligent-integration-platform-iip/reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - [Spark](https://www.g2.com/products/openclassrooms-spark/reviews) - [Spark](https://www.g2.com/products/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [SplashBI](https://www.g2.com/products/splashbi/reviews) - [Spotfire Analytics](https://www.g2.com/products/spotfire-analytics/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [ThoughtSpot](https://www.g2.com/products/thoughtspot/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/reviews) - [Zendesk Sunshine](https://www.g2.com/products/zendesk-sunshine/reviews) ## Databricks Features **Reports** - Reports Interface - Steps to Answer - Graphs and Charts - Score Cards - Dashboards **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics **Management** - Data dictionary - Data Replication - Query Language - Data Modeling - Performance Analysis **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Scalability and Performance - Generative AI Infrastructure** - AI High Availability - AI Model Training Scalability - AI Inference Speed **Customization - AI Agent Builders** - Natural Language Configuration - Tone Customization - Security Guardrails **Agentic AI - DataOps Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Decision Making **Traffic Management & Performance - AI Gateways** - Token-Aware Rate Limiting - Semantic Caching - Multi-Model Routing & Fallbacks **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Maintenance** - Data Migration - Backup and Recovery - Multi-User Environment **Security** - Access Control - Roles Management - Compliance Management **Operations** - Metrics - Infrastructure management - Collaboration **Analytics** - Analytics capabilities - Dasboard visualizations **Cost and Efficiency - Generative AI Infrastructure** - AI Cost per API Call - AI Resource Allocation Flexibility - AI Energy Efficiency **Functionality - AI Agent Builders** - Omni-channel Support - Agent Branding - Proactive Response Capabilities - Seamless Human Escalation **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Machine/Deep Learning Services** - Computer Vision - Natural Language Processing - Natural Language Generation - Artificial Neural Networks **Integrations** - Hadoop Integration - Spark Integration **Data Quality** - Data Preparation - Data Distribution - Data Unification **Machine/Deep Learning Services** - Natural Language Understanding - Deep Learning **Deployment** - On-Premise - Cloud **Security** - Data Encryption - User Access Control **Maintainence** - Data Quality Management - Policy Management **Management** - Cataloging - Monitoring - Governing **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Integration and Extensibility - Generative AI Infrastructure** - AI Multi-cloud Support - AI Data Pipeline Integration - AI API Support and Flexibility **Data and Analytics - AI Agent Builders** - Analytics & Reporting - Contextual Awareness - Data Privacy Compliance **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake **Performance ** - Scalability **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization **Security and Compliance - Generative AI Infrastructure** - AI GDPR and Regulatory Compliance - AI Role-based Access Control - AI Data Encryption **Integration - AI Agent Builders** - Workflow Automation - API Usage - Platform Interoperability - CRM Data Integration **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image **Generative AI** - AI Text Generation - AI Text Summarization **Usability and Support - Generative AI Infrastructure** - AI Documentation Quality - AI Community Activity **Agentic AI - Data Governance** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Decision Making **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services **Generative AI** - AI Text Generation - AI Text Summarization **Agentic AI - Data Science and Machine Learning Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support **Advanced Analytics & Modeling - Analytics Platforms** - Data Modeling and Governance - Notebook and Script Integration - Built-in Predictive and Statistical Models **Agentic AI Capabilities - Analytics Platforms** - Auto-generated Insights and Narratives - Natural Language Queries - Proactive KPI Monitoring and Alerts - AI Agents for Analytical Follow-ups **Personalized Intelligence - Analytics Platforms** - Behavioral Learning for Contextual Query Refinement - Role-based Insight Personalization - Conversational and Prompt-based Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications ## Top Databricks Alternatives - [Cloudera Data Platform](https://www.g2.com/products/cloudera-cloudera-data-platform/reviews) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.6/5.0 (687 reviews) - [Teradata Vantage](https://www.g2.com/products/teradata-teradata-vantage/reviews) - 4.3/5.0 (345 reviews)