--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1348 reviews by the users' company size, role or industry to find out how Databricks works for a business like yours. aggregate_rating: rating_value: 4.6 review_count: 1348 scale: '5' date_modified: '2026-07-22' parent_category: name: Big Data url: https://www.g2.com/categories/big-data --- # 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:** 1,348 ## 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 praise the **ease of use** and **comprehensive features** of Databricks for data warehousing and ML applications. (192 reviews) - Users praise the **ease of use** of Databricks, enhancing their experience with intuitive interfaces and reliable services. (154 reviews) - Users appreciate the **seamless integrations** of Databricks with AWS and other tools, enhancing daily operations and efficiency. (141 reviews) - Users value the **seamless collaboration** offered by Databricks, enhancing teamwork on data projects with real-time insights. (114 reviews) - Users praise the **integrated analytical features** of Databricks, enhancing collaborative data processing and insight visualization. (112 reviews) - Scalability (111 reviews) - ML Integration (106 reviews) - Users appreciate the **easy integrations** of Databricks, seamlessly connecting with cloud infrastructure and enhancing data management. (102 reviews) - Machine Learning (97 reviews) - Users value the **effective data management features** of Databricks, simplifying their workflows and enhancing decision-making. (87 reviews) **What users dislike:** - Users note a **steep learning curve** initially, with confusing permissions and compute modes affecting usability. (78 reviews) - Users note that the **costs can be quite high** for utilizing Databricks effectively, especially for large data projects. (71 reviews) - Users find a **steep learning curve** with Databricks, especially challenging for newcomers to big data tools. (64 reviews) - Users find the **complexity** of Databricks challenging, especially for smaller teams and initial setup processes. (45 reviews) - Users face **complex setup** challenges initially, though support helps simplify the experience over time. (35 reviews) - Performance Issues (34 reviews) - Users face **unintuitive UI issues** that lead to random errors and complicate the experience for non-technical users. (34 reviews) - Poor UI Design (33 reviews) - Users express frustration over **missing features** in Databricks, limiting its effectiveness for complex deployments and custom setups. (31 reviews) - Cost (29 reviews) ## Databricks Reviews ### 1. Databricks Boosts Productivity with a Unified Workspace and AI-Assisted Development **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** May 27, 2026 **What do you like best about Databricks?** As an ADE, what I like most about Databricks is that it removes infrastructure friction, so I can focus purely on data engineering logic. I also really appreciate the unified workspace: I can write PySpark for data extraction and transformation, switch to SQL for exploratory analysis, and review data lineage, all within a single browser tab is a huge productivity boost. On top of that, the built-in AI features have been incredibly helpful because they let me worry less about syntax and spend more time on the logic itself. Finally, with the seamless integrations through Lakehouse Federation and the straightforward onboarding, my work has become much smoother. **What do you dislike about Databricks?** While the platform is excellent for development, the DBU consumption model and cluster management can feel a bit daunting at my level. As a beginner, I spent a lot of time testing different bits of logic, and it was easy to forget to terminate the all-purpose cluster afterward, which led to minimal but still unnecessary credit consumption. Thankfully, auto-termination exists and helped keep credits from disappearing. Still, a more aggressive auto-termination setting or a smarter pause feature would make it easier to avoid any credit loss in the first place. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the local environment hell-hole that often slows down junior engineers. By providing a ready-to-use Lakebase architecture, it lets me practice enterprise-level data engineering without needing to connect to VPNs or deal with complex Docker setups. In my project, it addressed the full flow: ingesting raw data, transforming it, and serving it for analytical queries. This also benefits my team, because I can onboard onto real data pipelines much faster and start contributing sooner. At the same time, I’m learning how to build production-ready ETL workflows without my senior teammates having to spend hours helping me troubleshoot my local Python/Spark environment. An unexpected benefit was how seamless collaboration is. Because the notebooks are cloud-based and ties to the workspace, sharing my project with senior engineers for code reviews was as simple as sending a link. Additionally, the way Databricks handles metadata made me realize early in my career how important data governance is. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been such a productivity boost for you! The unified workspace and AI-assisted development are indeed powerful features that many of our users appreciate. We understand your concerns about the DBU consumption model and cluster management. We're constantly working to improve the user experience, and your feedback will be taken into consideration for future enhancements. ### 2. Centralized Governance, Powerful Migration Tool **Rating:** 5.0/5.0 stars **Reviewed by:** Jaswanth J. | Data Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** May 26, 2026 **What do you like best about Databricks?** I like the Unity Catalog as a single governance layer which centralizes access control and offers fine-grained permissions across data assets. The workspace API and automation features are valuable for streamlining operations. I appreciate that volumes replace mounts, improving security with credential-free access. The Lakehouse Federation simplifies cost consolidation and reduces data movement costs. Having Photon and ML Runtime on the same platform enhances operational efficiency. The initial setup was user-friendly, thanks to the guidance from the Databricks portal. **What do you dislike about Databricks?** * Migration tooling is manual and fragmented * Mount-to-Volume path conversion has no automated path * Cluster security mode NONE still exists * Hive metastore and UC coexist awkwardly * Custom WHL libraries on mount lack a clean upgrade path **What problems is Databricks solving and how is that benefiting you?** Databricks provides centralized access control with fine-grained permissions, identity-based access without exposing storage credentials, unified data discovery and lineage, and reduces operational overhead by consolidating platforms and managing data more efficiently. **Official Response from Jess Darnell:** > We're glad to hear that you appreciate the Unity Catalog and the workspace API for streamlining operations. We understand your concerns about the manual and fragmented migration tooling, and we are continuously working to improve this aspect of our platform. ### 3. Efficient Data Management, Needs More Granular Permissions **Rating:** 3.0/5.0 stars **Reviewed by:** Matt G. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks to manage my enterprise data, and it solves the issue of sharing data at scale for reporting and analytics with my customers. I value Delta sharing, now called open sharing, because it allows me to share data at scale without requiring customers to set up specific APIs or virtual pipelines. I also think that the initial setup of Databricks was fairly easy. **What do you dislike about Databricks?** I don't like how Databricks lacks the ability to share data with more granular permissions. The ability for delta sharing to take predicate pushdowns and enable that in data sharing to customers would be helpful. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to manage my enterprise data and solve the issue of sharing data at scale for analytics with my customers. I value the Delta sharing feature, as it allows data sharing without needing specific APIs or pipelines. **Official Response from Janelle Glover:** > Thank you for sharing your experience with Databricks. We're pleased to hear that the initial setup was easy and that Delta sharing has been valuable for sharing data at scale. We understand your feedback about the need for more granular permissions and will take it into consideration for future improvements. ### 4. Fast, Efficient Databricks with Strong Ecosystem Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Johnson C. | Software Engineering Intern - Data Science, Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I love how databricks closely integrated with the broader data ecosystem, and it runs fast and efficiently. It makes it easier to integrate new features and bring new additions into the ecosystem whenever something comes up, or whenever issues need to be addressed. **What do you dislike about Databricks?** March I think it has a lot of integration a lot of tools. I just hope that one thing they could do that they could build some sort of like an agent skillet. They already have a skill set, but like Aiden skills so they can teach me that potentially have already exists and I know they have really good MCPs and skills for the agent to use already. **What problems is Databricks solving and how is that benefiting you?** It is solving how I trained the model how I run the motor pipelines and how I store and save the track of model training process **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the strong ecosystem integration and the fast and efficient performance of Databricks. We appreciate your feedback and will take your suggestion for an agent skill set into consideration for future improvements. ### 5. An All-in-One Platform for Data, Analytics, and Machine Learning **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Entertainment | Enterprise (> 1000 emp.) **Reviewed Date:** May 29, 2026 **What do you like best about Databricks?** I really value that this platform supports everything from raw data ingestion and SQL analytics to machine learning with notebooks. It’s not just another external tool; it feels like a fully integrated solution for an entire organization. I also appreciate that it’s designed to support both technical and business users. **What do you dislike about Databricks?** Managing costs and optimizing cluster usage can sometimes be challenging and requires internal knowledge of the underlying architecture, such as CPU and RAM configuration for jobs. This can significantly impact the overall budget, especially for small companies. **What problems is Databricks solving and how is that benefiting you?** Databricks helps us process millions of records daily in a reasonable amount of time while maintaining scalability for our solutions. It also allows us to build and integrate solutions not only within Databricks itself, but also by deploying external packages. In addition, the command-line tools provide flexibility to integrate with our current CI/CD workflow, helping us reduce deployment times. **Official Response from Jess Darnell:** > We appreciate your feedback on the benefits of Databricks for processing large volumes of data and integrating with external packages. We understand the impact of cost management on small companies and are focused on providing solutions to address this concern. ### 6. 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. ### 7. Unified Data Platform with Fantastic Usability **Rating:** 5.0/5.0 stars **Reviewed by:** Jared C. | Undergraduate Research Assistant, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like how tightly integrated everything is in Databricks. My company's data and the machine learning experiments I need to do are right there, along with the AI BI dashboards, which are easily accessible and shareable. It was super easy to set up, as my boss just added me to a credential list and I got access immediately. Databricks unifies compute and storage in a way I've never had before, making it really easy to run queries efficiently, quickly, accurately, and securely. **What do you dislike about Databricks?** Configuring the compute can be a little bit challenging, and sometimes, it's difficult to access the resources that I need. But when everything comes together, it works nicely. **What problems is Databricks solving and how is that benefiting you?** Databricks unifies compute and storage, making it easy to run queries efficiently, quickly, accurately, and securely. It meets all my company’s data science and engineering needs. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We appreciate your feedback about the challenges with configuring the compute, and we are committed to enhancing the platform to address these issues. ### 8. Consolidated Our Data Stack with Databricks that Boosted Performance and Productivity **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Retail | Enterprise (> 1000 emp.) **Reviewed Date:** May 14, 2026 **What do you like best about Databricks?** Coming from an Airflow + Snowflake setup, moving to Databricks removed a layer of coordination overhead we had normalized, jobs scheduling jobs, reverse ETL pipelines just to get analytical results back into operational systems, and a separate feature store drifting out of sync with training data. The integrations were a big part of why the transition was smoother than expected: native connectors for cloud storage, Git-based repo sync for version-controlled notebooks, and the Databricks SDK plugging cleanly into our existing CI/CD pipelines meant we weren't rebuilding everything from scratch. Databricks Workflows replaced our Airflow DAGs cleanly, Unity Catalog gave us lineage and access control across our full medallion architecture without a separate tool, and Lakebase let us retire the online feature store entirely since model features now live where the data already is. Performance on large-scale aggregations across our brick-and-mortar store datasets improved noticeably, and the workspace UI makes it easy for the whole team to navigate notebooks, pipelines, and catalog without context-switching. The AI-assisted features in the notebook environment genuinely speed up development. The autocomplete and error suggestions that understand the data context are more useful than they sound day-to-day. Onboarding new engineers was also faster than expected given the depth of the platform, with thorough documentation and a responsive support team during migration. From an ROI standpoint, consolidating tooling meant fewer vendor contracts, less pipeline maintenance, and engineering time redirected toward actual product work. **What do you dislike about Databricks?** The cost model is the most persistent friction point — compute costs can escalate quickly if cluster lifecycle management isn't tight, and for a team that's still maturing its governance around who spins up what, the billing visibility could be more granular out of the box. The UI, while generally clean, gets harder to navigate at scale; when you have dozens of workflows, notebooks, and catalogs, the workspace organization tools don't quite keep up with the sprawl. On the integrations side, some third-party connectors feel like they were added as an afterthought — the experience isn't always as seamless as the native ones, and occasional version compatibility issues have caused unexpected debugging time. Performance on very large unoptimized queries can still surprise you with cold start latency on serverless compute, which matters when you're iterating quickly during development. The AI assistant features are improving but still inconsistent — context awareness drops off on complex multi-file projects and the suggestions occasionally miss the mark in ways that slow you down rather than help. Support response quality has been good for critical issues, but for nuanced technical questions the first response is sometimes generic, and getting to someone with deep product knowledge takes an extra round of escalation. **What problems is Databricks solving and how is that benefiting you?** The core problem we were solving was operational sprawl — we had analytical data living in one place and operational data in another, with a fleet of pipelines just to keep them in sync. Working with high-volume brick-and-mortar store data across a medallion architecture, the performance gains on large aggregations alone justified the move; queries that previously required careful warehouse sizing now handle gracefully on autoscaling compute. Consolidating onto one platform also meant our AI and ML workflows stopped being second-class citizens — feature engineering, model training, and serving now happen in the same environment where the data lives, which removed an entire category of infrastructure we were maintaining. The workspace UI, while not perfect at scale, made it easier to onboard the broader team without everyone needing deep platform expertise to be productive from day one. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has had such a positive impact on your data stack, boosting performance and productivity. It's great to know that the integrations, performance improvements, and AI-assisted features have made such a difference for your team. We appreciate your feedback and are committed to continuously improving our platform. ### 9. High-Performance Analytics with Databricks SQL and Unity Catalog Governance **Rating:** 4.0/5.0 stars **Reviewed by:** Arpit V. | Data Platform Engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** Databricks delivers excellent performance when working with large volumes of data. The Databricks SQL environment also makes it easier for business users and analysts to explore insights without having to rely so heavily on engineering teams. Features such as Unity Catalog strengthen governance and simplify managing access across departments. **What do you dislike about Databricks?** The learning curve can feel steep, especially for users coming from traditional data warehouse solutions. In my experience, query optimization can also take some extra effort, since certain workloads require additional tuning to get the best results. **What problems is Databricks solving and how is that benefiting you?** We needed a solution that could handle both data warehousing and advanced analytics without creating new data silos. Databricks has helped us centralize our data assets while still maintaining strong governance. As a result, teams can access trusted data more quickly and build reports with fewer delays, which has improved decision-making across the organization. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been delivering excellent performance for your large volumes of data and that the SQL environment is making it easier for business users and analysts to explore insights. ### 10. Seamless Data Integration, Amazing Customer Service **Rating:** 5.0/5.0 stars **Reviewed by:** Alwarda F. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I appreciate the seamless integration that Databricks offers, making it very easy to use and allowing it to integrate with different systems. It's great for automating code and building pipelines, and the customer service is amazing. I think the apps are amazing too. Databricks is one of the most self-intuitive products, and the integration process is straightforward. The learning curve is steep initially, but it becomes easier as you progress. **What do you dislike about Databricks?** I would love to see that database has been generated within Databricks so I don't have to move my data outside of Databricks. I can build the graphs and connect it to Genie's. I think that's missing. From data. **What problems is Databricks solving and how is that benefiting you?** Databricks integrates data seamlessly from different sources, is easy to use, and integrates well with other systems. It automates code development, enhancing efficiency. **Official Response from Jess Darnell:** > We're happy to hear that you are benefiting from Databricks' seamless data integration and automation capabilities. We understand your suggestion about generating databases within Databricks and will take it into consideration for future improvements. Thank you for your valuable input! ### 11. Streamlined AI and Data Solutions with Swift Setup **Rating:** 4.5/5.0 stars **Reviewed by:** Amrendra S. | Solution Delivery Lead, Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks as a mainstream tool to implement native AI and data solutions worldwide and for Accenture. It's heavily used in engagements to enable data analytics for our clients. The Accelo Unity Catalog is valuable as it helps accumulate metadata and facilitates analytics. The deployment scripts provided make it easy to deploy in our environment quickly, which is great since it allows us to start working immediately. I also find the Genesys catalog's support for AI agents fantastic, greatly aiding adoption. **What do you dislike about Databricks?** The aspect of the unit bridge used to be an issue for us when integrating different stacks and AI agents. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to implement AI and data solutions, enabling data analytics for clients. It simplifies deployment with scripts making it easy to start working in different environments. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in enabling data analytics for your clients. We appreciate your feedback on the deployment process and the support for AI agents. ### 12. 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. ### 13. Streamlined Automation with Seamless Workflow Integration **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Small-Business (50 or fewer emp.) **Reviewed Date:** July 18, 2026 **What do you like best about Databricks?** I use Databricks to manage workflows and automate tasks, which has been really productive for my team over the past two years. It handles visualization tasks and manages different functions smoothly, with a high response time. I prefer Databricks for documentation, automation, data mining, and lead gen analysis, which helps in getting a higher ROI by syncing different tasks within one main parent workflow. Its speed and ease of use, along with no credit-based system, multiple integrations, and easy data extraction and sharing capabilities make it a must-have tool. **What do you dislike about Databricks?** I think there are not a lot as it is one of the best firm but maybe a better informational content can help in easy education as compared to the onboarding process we had but had to watch multiple videos on YouTube and read articles by the firm after it's launch and we went live with the product. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for managing workflows and automating tasks, solving visualization issues, syncing tasks into a parent workflow, and integrating with tools. It enhances productivity, offers high response time, and supports data mining and lead gen analysis. **Official Response from Jess Darnell:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that our platform has been instrumental in managing workflows, automating tasks, and providing seamless integration with other tools. We'll definitely take your feedback on informational content into account for enhancing user education. ### 14. Everything Under One Roof with Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Shradha C. | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I think Databricks is great because it's everything under one platform. It includes MLflow and Spark, and it's really easy to use. I love the architecture and how it integrates with S3 buckets. Integration becomes very easy; our streaming data comes to S3 bucket as Parquet files, allowing transformations in the prepared layer and effortless data extraction for the analytics team. It truly acts as a one-stop shop for all my data-related issues. **What do you dislike about Databricks?** I think, I don't like Genie. Genie doesn't give me a good response. So I would like DataOps to improve Genie. **What problems is Databricks solving and how is that benefiting you?** Databricks acts as a one-stop shop for data-related issues, integrating all data into a data lake and managing pipelines. It eases integration and data transformation, allowing our analytics team to extract useful insights efficiently. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks to be a one-stop shop for all your data-related needs, with its seamless integration and ease of use. We appreciate your feedback about Genie, and we will certainly take it into consideration for future improvements. ### 15. Streamlines Data with Speed, Needs Real-Time Processing Boost **Rating:** 5.0/5.0 stars **Reviewed by:** Prasant R. | Software Application Development Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I find Databricks very useful for collecting and processing data, as it helps us produce valid data for usage. It's awesome for us because it assists in obtaining data with the middle and architecture, providing data in a raw layer and then converting it for real usage. Databricks processes data pretty fast and keeps it in a very structured manner, allowing us to query the data online and on time. The team from Databricks was quite helpful in establishing the entire infrastructure. **What do you dislike about Databricks?** The only area where we need improvement is to process the real-time data and produce the data in a real-time environment. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to streamline data analysis. It helps collect, process, and produce valid data for usage, and processes data quickly in a structured manner. **Official Response from Janelle Glover:** > Thank you for sharing your experience with Databricks. We will take your feedback into consideration as we continue to improve our services. ### 16. Effortless ETL with Databricks **Rating:** 5.0/5.0 stars **Reviewed by:** Pawan K. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks for its serverless and managed ETL capabilities. It reduces the barrier to entry, so you don't have to have deep Spark or Hadoop skills, which is great. I have practically seen how quickly one can set up and get going, which I find very easy for serverless setups. **What do you dislike about Databricks?** The security posture could be improved, especially by having more private connectivity rather than public endpoints. I would like to have a custom domain name enforced from our corporate environment to mitigate data egress risks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to ingest data from various on-prem data sources. It reduces the barrier to entry by not requiring deep skills in Spark or Hadoop. The serverless and managed ETL setup quickly gets us going, making it very easy for us. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks' serverless and managed ETL capabilities beneficial and easy to use. We appreciate your feedback on the security posture, and we are continuously working to improve in that area. ### 17. 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. ### 18. 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. ### 19. User-Friendly with Robust Performance **Rating:** 5.0/5.0 stars **Reviewed by:** Suyog P. | Business Intelligence Developer/Data Engineer(Microsoft), Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I think Databricks is a great tool for data analytics, AI, ML, and other data-related work. I love how we can use the same dashboard they provide for reporting and insights. I really like the performance and the user interface is great—it's user-friendly for any engineer, making it easy to build whatever comes out of the engineering mind. **What do you dislike about Databricks?** Accessing the Unity Catalog settings from the notebook or SQL script page could be improved. This would be a great feature to have. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for data analytics, AI, ML, and reporting using their dashboard. It offers great performance and a user-friendly UI that makes engineering tasks easy. Switching to Databricks improved our data handling and performance, although accessing the catalog could be streamlined. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks user-friendly and that it has improved your data analytics, AI, and ML work. We appreciate your feedback about accessing the Unity Catalog settings and will take it into consideration for future improvements. ### 20. 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. ### 21. Powerful platform for Data analytics **Rating:** 4.0/5.0 stars **Reviewed by:** Aditya Y. | Student, Small-Business (50 or fewer emp.) **Reviewed Date:** June 01, 2026 **What do you like best about Databricks?** For me and my team Databricks brings data engineering as well as analytics and machine learning into one platform . Genuinely speaking it makes our work easy with large datasets . their pricing is also good **What do you dislike about Databricks?** Honestly saying some advanced features of Databricks can be difficult to configure initially or simply I should say the initial setup can be complex for beginners **What problems is Databricks solving and how is that benefiting you?** For our team it helps manage and analyze large datasets efficiently I must say It improves collaboration between teams and makes it easier to generate insights for business decisions . **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been a powerful platform for your data analytics needs, combining data engineering, analytics, and machine learning in one place. We appreciate your feedback on the pricing as well. ### 22. Outstanding Databricks Data + AI Summit Experience: Innovation, Vision, and Great Connections **Rating:** 5.0/5.0 stars **Reviewed by:** Ahmed M. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Day 1 of 3 at the Databricks Data + AI Summit 2026, and it has already been an outstanding experience. The energy, innovation, and vision on display have been impressive. The discussions around Agentic AI, Data Intelligence, AI governance, digital twins, and enterprise-scale AI adoption reinforced how quickly the industry is evolving from experimentation to real business impact. Beyond the technology, one of the biggest highlights has been connecting with industry leaders, practitioners, customers, and partners who are shaping the future of Data and AI. Looking forward to the next two days of keynotes, product announcements, technical deep dives, and great conversations. #Databricks #DataAISummit #AI #DataEngineering #MachineLearning #GenerativeAI #AgenticAI #DataIntelligence **What do you dislike about Databricks?** Nothing everything. Was just perfect.. thank you **What problems is Databricks solving and how is that benefiting you?** Everything data and ai **Official Response from Janelle Glover:** > We're thrilled to hear that you had an outstanding experience at our Data + AI Summit 2026! It's great to know that you found the energy, innovation, and vision impressive, and that you had the opportunity to connect with industry leaders and practitioners. ### 23. Streamlined Data Architecture & AI Solutions **Rating:** 4.0/5.0 stars **Reviewed by:** Siddhesh S. | Business Intelligence Developer, Mid-Market (51-1000 emp.) **Reviewed Date:** September 12, 2024 **What do you like best about Databricks?** I love how easy it is to integrate multiple data workloads such as Data Warehouse, Data Lakes, and Model Registry for the organization in a single interface with Databricks. **What do you dislike about Databricks?** I think working with Catalog still requires a different tab open. If we can integrate it into the multiple tabs section within Databricks UI, it would reduce context switching and would help users to stay focused and productive. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to create workflows for data architecture and AI solutions, solving the problem of maintaining multiple workloads. It's easy to integrate data warehouses, lakes, and registry in one interface. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with the Databricks Data Intelligence Platform. We understand the frustration with updating single tables from multiple threads and will work to address this in future updates. ### 24. Efficient Setup, Needs Better Unstructured Data Support **Rating:** 4.0/5.0 stars **Reviewed by:** Leo W. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks because it's cost-effective and makes it easy to build agents. The data retrieval is quick and agent building is straightforward. The setup process is not challenging, making it easy to get started without any hassles. **What do you dislike about Databricks?** I find the support for unstructured data lacking. It's not easy to build a search engine based on unstructured data like email content, policy PDF documents, and finance Excel files. I also have concerns about the query performance. The vector search is pretty slow, especially with reasoning questions, as opposed to lookup questions where the performance is good. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for quick data retrieving and easy agent building for our data warehouse, helping provide insights to gain more customers. **Official Response from Jess Darnell:** > It's great to hear that Databricks is helping you with quick data retrieval and easy agent building for your data warehouse, ultimately providing insights to gain more customers. Thank you for your feedback on the support for unstructured data. We are constantly working to improve our platform and will take your concerns into consideration. ### 25. 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! ### 26. User-Friendly, Affordable Data Processing at Scale with Fast Support **Rating:** 5.0/5.0 stars **Reviewed by:** Nanda M. | Junior Data Engineer , Information Technology and Services, Small-Business (50 or fewer emp.) **Reviewed Date:** May 27, 2026 **What do you like best about Databricks?** It has a user-friendly interface and integrates with other clouds easily. We can process TBs of data without much effort. Compared to other data-processing tools, its price is lower. It also includes Ginee AI, and by using that we can handle data processing much more easily. If we face any issues, they solve the problem in less time. **What do you dislike about Databricks?** it's is very difficult to use for new users **What problems is Databricks solving and how is that benefiting you?** i'm a data engineer so i'm using this for process the TB's of the data. for ML Flows. integrate with data science, analytics team **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks user-friendly, affordable, and supportive for processing large amounts of data. We appreciate your feedback and are continuously working to improve the user experience for new users. ### 27. Databricks: A Unified Data and AI Platform **Rating:** 5.0/5.0 stars **Reviewed by:** Arvind N. | SVP-Data and Analytics -Lifesciences, Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I find the Lakehouse feature of Databricks awesome. It's really helpful in simplifying data management by integrating data engineering, BI, and machine learning into a single platform. This has reduced our manual ETL efforts significantly and improved collaboration between the business and technical teams. The platform has enabled us to modernize data engineering and analytics initiatives, while providing improved governance and data discovery. Also, the dynamic viewing and AI capabilities allow business users to interact with data using natural language, which is fantastic. **What do you dislike about Databricks?** The integration during migration from legacy systems to Databricks could be improved, specifically regarding the natural language coordination. **What problems is Databricks solving and how is that benefiting you?** Databricks reduces manual efforts, accelerates development, enhances collaboration, and unifies data engineering, BI, and machine learning. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks! We're glad to hear that the platform has helped modernize your data engineering and analytics initiatives while improving collaboration and governance. We appreciate your feedback on the migration integration, and we'll take that into consideration for future improvements. ### 28. 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. ### 29. Solves Developers’ Problems with Genie, Lakeflow Connect, and DLT **Rating:** 5.0/5.0 stars **Reviewed by:** Shreeram P. | MIS & Customer Retention, Mid-Market (51-1000 emp.) **Reviewed Date:** April 30, 2026 **What do you like best about Databricks?** This platform solves developers’ problems by offering features like Genie, Lakeflow Connect, and DLT. **What do you dislike about Databricks?** Before using it, I want to understand the compute and charges, and how to use it properly. Basically, I need to learn a lot first. **What problems is Databricks solving and how is that benefiting you?** It solved our data pipeline and dashboard creation challenges. With SDP and AI/BI Genie, we moved from manually managing the data pipeline to simply declaring it in SQL and having everything handled for us. Instead of spending so much time building dashboards, we can now just ask questions in natural language and get the answers we need without wasting a lot of time. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has been able to solve your data pipeline and dashboard creation challenges with features like Genie, Lakeflow Connect, and DLT. ### 30. Effortless Data Handling with ML Capabilities **Rating:** 4.5/5.0 stars **Reviewed by:** Shweta T. | Procurement Officer, Hospital & Health Care, Enterprise (> 1000 emp.) **Reviewed Date:** May 31, 2026 **What do you like best about Databricks?** I like how Databricks handled parabytes of raw information effortlessly, blending storage and compute in a way that eliminated data silos and version conflicts. The built-in machine learning capabilities turned multi-week projects into something I could prototype in days, complete with seamless model tracking and development that gave me confidence in production environments. The initial setup and integration with tools was smoother than expected. **What do you dislike about Databricks?** I often found the interface overwhelming during deeper explorations with so many layered options. I had to rely on the knowledge base to overcome the difficulties. **What problems is Databricks solving and how is that benefiting you?** Databricks eliminated data silos and version conflicts, handling large volumes of information effortlessly. It transformed lengthy machine learning projects into rapid prototypes with seamless model tracking and development, boosting my confidence in production environments. **Official Response from Jess Darnell:** > It's great to hear that Databricks has helped you eliminate data silos and version conflicts, making your machine learning projects more efficient and boosting your confidence in production environments. ### 31. Seamless Integration and Scalable Performance with Room for UI Improvement **Rating:** 4.0/5.0 stars **Reviewed by:** Ashley F. | Senior Executive, Small-Business (50 or fewer emp.) **Reviewed Date:** April 20, 2026 **What do you like best about Databricks?** I use Databricks to build ETL pipelines and process large-scale data with Spark. I like Databricks most for its seamless integration with Apache Spark, collaborative notebooks, and its ability to handle large-scale data processing efficiently in a unified platform. The seamless Apache Spark integration lets me process huge datasets quickly without worrying about cluster setup, while collaborative notebooks make it easy to work with my team in real-time. The scalable architecture ensures reliable performance even with heavy data workloads. The initial setup of Databricks was fairly straightforward, especially with cloud integration. **What do you dislike about Databricks?** The UI can feel a bit cluttered at times, cluster startup times can be slow, and the pricing can get expensive for smaller projects or prolonged usage. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to efficiently process large-scale data, simplify ETL workflows, and collaborate with my team in a unified environment, gaining faster data-driven insights. **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the seamless integration with Apache Spark and the collaborative features of Databricks. We appreciate your feedback on the UI and cluster startup times, and we are continuously working to improve these areas. Regarding pricing, we offer various options to accommodate different project sizes and usage durations. Thank you for sharing your experience with us! ### 32. Centralized Data Management with Databricks **Rating:** 4.5/5.0 stars **Reviewed by:** Vikram P. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I agree with using Databricks because before it, managing Azure function apps was painful due to scalability issues. After moving to Databricks, dealing with jobs, workflows, and using declarative pipelines resulted in less overhead for operational teams, and it just runs smoothly. **What do you dislike about Databricks?** Databricks releases features too early, which is problematic. For example, Unity Catalog lacks high availability in disaster recovery even now. The Managed ER only works for managed tables and does not support external tables, which forced us to design a scalable solution on our own. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to streamline data ingestion and transformation. It connects with various file formats, aiding in data curation and sharing with downstream consumers through Unity Catalog. **Official Response from Jess Darnell:** > We're glad to hear that Databricks has helped streamline your data management and operational processes. We appreciate your feedback about the early release of features and the limitations you've experienced. We're constantly working to improve our platform and your input is valuable in helping us prioritize our efforts. ### 33. BI and Data Engineering in One Place, with AI-Assistant **Rating:** 4.0/5.0 stars **Reviewed by:** Corrado P. | Service Designer and Workshop Facilitator, Enterprise (> 1000 emp.) **Reviewed Date:** April 23, 2026 **What do you like best about Databricks?** Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems. Using AI to improve and make faster the SQL writing and execution **What do you dislike about Databricks?** Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so. **What problems is Databricks solving and how is that benefiting you?** Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data. **Official Response from Jess Darnell:** > It's great to hear that Databricks is solving fragmentation and inefficiency across the data lifecycle for you, and that it's removing friction between teams, tools, systems, and data. ### 34. Transforms Table Data into Trustworthy Visuals with Helpful Debugging **Rating:** 4.5/5.0 stars **Reviewed by:** Aruthra L. | Data Engineer, Logistics and Supply Chain, Enterprise (> 1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** I like the concept of transforming data into visuals for each table. Genie Code also helps with debugging and validating the data, which makes it easier to trust what I’m working with. **What do you dislike about Databricks?** As a proprietary platform built on open-source foundations, it can still introduce vendor lock-in risks, particularly through components such as Unity Catalog and its custom APIs. **What problems is Databricks solving and how is that benefiting you?** Databricks primarily solves the longstanding challenges of fragmented data architectures by introducing the Lakehouse paradigm. It combines the low-cost, scalable storage of data lakes with the reliability, ACID transactions, and performance of traditional data warehouses. This eliminates data silos, reduces costly ETL duplication, and provides a single unified platform for structured, semi-structured, and unstructured data. **Official Response from Janelle Glover:** > Thanks for sharing your feedback! We're glad to hear that Databricks is helping you solve challenges associated with fragmented data architectures and that you find Genie Code helpful for debugging and validating data. ### 35. Comprehensive Analytics with Smooth Workflows **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I use Databricks for end-to-end analytics and pipeline development for machine learning. I love that it provides the whole analytical pipeline and machine learning workflows from exploratory data analysis to serving and monitoring, all in one place. It has made workflows and jobs run extremely smoothly and reliably. The initial setup was extremely easy, so much so that I thought I might be doing something wrong. Overall, I rate it a 10 out of 10 for recommending it to a friend or colleague. **What do you dislike about Databricks?** RBAC could be simplified. Maybe it is our infrastructure but we would like to be able to use UI and define role-based and access-based authentication to schema, tables, and columns. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for end-to-end analytics and machine learning pipelines. It makes workflows and jobs run smoothly and reliably, providing a complete analytical pipeline and ML workflows from EDA to serving and monitoring all in one place. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been instrumental in streamlining your analytics and machine learning workflows. We appreciate your feedback about RBAC and will take it into consideration for future improvements. ### 36. Databricks Streamlined Our ETL Migration with Delta Lake and Unified Analytics **Rating:** 3.5/5.0 stars **Reviewed by:** Yuvi M. | Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** Databricks transformed my day-to-day workflow, taking me from constant SQL Server/ADF headaches to scalable, unified analytics. Migrating stored procedures into Spark SQL notebooks was surprisingly smooth, and using Delta Lake MERGE instead of complicated UPDATE logic saved me weeks of rewriting. The most helpful features for me have been Delta Lake’s ACID transactions and schema evolution, which handle my sparse shipment loads really well. Unity Catalog has also been a big win because it eliminates the back-and-forth of RDS access tickets by enabling governed table sharing. On top of that, Genie turns natural-language requests into production-ready Spark SQL almost instantly. On the upside, autoscaling clusters have cut costs by about 70% compared with ADF’s always-on pipelines. I also like being able to combine PySpark and SQL in a single notebook, which makes complex joins and subqueries much easier to manage. And I don’t miss the old NOLOCK hint debates—built-in optimizations take care of that. If you’re migrating ETL pipelines, Databricks removes a lot of the SQL-to-cloud friction while still scaling to enterprise volumes without breaking the bank. **What do you dislike about Databricks?** The cluster reconnects fairly often, which can be disruptive during active work sessions. Also, when I run complex or heavy queries, I notice clear lag in response times, and that slowdown can hurt productivity. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us centralize our data engineering and analytics workflows into a single, unified platform. It addresses the challenge of managing large-scale data pipelines by enabling our team to process and transform massive datasets efficiently with Spark. The collaborative notebook environment has also boosted productivity, making it easier for data engineers and analysts to work together. Overall, it has significantly reduced the time we spend on data preparation and has allowed us to focus more on deriving insights. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks, especially regarding the smooth migration process, cost savings with autoscaling clusters, and your experience with Genie and Unity Catalog. We understand your concerns about cluster reconnects and query response times, and we're actively working to enhance the platform's performance for all users. ### 37. Unified Lakehouse with Unity Catalog Makes Governance and Collaboration Seamless **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Accounting | Small-Business (50 or fewer emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** Unified Lakehouse Architecture: Bringing together the best elements of data warehouses and data lakes in a single platform feels like a massive game-changer. It removes the burden of maintaining separate, siloed infrastructures for relational BI queries versus raw, unstructured data storage, which in turn lowers total cost of ownership and reduces overall engineering complexity. Centralized Governance with Unity Catalog: Handling security, access controls, and automated data lineage from one interface across all workspaces has dramatically simplified compliance. The ability to trace data downstream—from raw ingestion all the way to the final BI report or machine learning model—adds a lot of confidence in data integrity. Persona-Specific Collaborative Workspaces: The platform also does a great job supporting multiple technical disciplines without forcing teams into separate tools. Data engineers can build robust pipelines with multi-language notebooks, data scientists can manage the ML lifecycle natively through integrated MLflow, and business analysts can run high-performance queries using Databricks SQL, all while working at the same time on the exact same live datasets. **What do you dislike about Databricks?** Steep Learning Curve: Getting up to speed on the platform takes solid foundational knowledge of Apache Spark, cloud infrastructure, and languages like Python or Scala, which can make initial onboarding difficult for less technical team members. Complex Cost Governance: Cloud compute spend can rise quickly if cluster auto-termination settings, node sizing, and auto-scaling policies aren’t monitored closely and kept under tight control. Interface and Feature Transition Overhead: Because the platform evolves quickly, updates can sometimes lead to a fragmented UI experience, especially when moving workflows from legacy configurations to newer frameworks like Unity Catalog. **What problems is Databricks solving and how is that benefiting you?** Data Silos: Historically, organizations had to maintain a data lake for raw, unstructured data and a separate data warehouse for structured business intelligence. Databricks addresses this with its Lakehouse architecture, bringing both together into a single storage and performance layer. Team Fragmentation: Data engineers, data scientists, and business analysts often end up working in isolated tools and workflows. Databricks offers a collaborative workspace where these disciplines can work side by side on the same live datasets, using SQL, Python, Scala, or R. Infrastructure Complexity: Configuring, scaling, and managing distributed computing environments manually can be highly complex and time-consuming. Databricks automates cluster management, auto-scaling, and environment configuration so teams can stay focused on the data itself rather than ongoing server maintenance. **Official Response from Aunalisa Arellano:** > Thank you for sharing your detailed feedback on Databricks! We're thrilled to hear that you find our Unified Lakehouse Architecture and Unity Catalog beneficial for simplifying governance and collaboration in your data workflows. We understand your concerns about the learning curve, cost governance, and interface transitions. We continuously strive to improve user experience and provide resources to support all team members in effectively utilizing our platform. Your insights are valuable, and we appreciate your feedback. If you have any specific questions or need assistance with any aspect of Databricks, please feel free to reach out. We're here to help and ensure you have a seamless experience with our platform. ### 38. Scalable Power with Manageable Trade-offs **Rating:** 4.5/5.0 stars **Reviewed by:** Janani D. | Senior Data Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** The collaborative notebooks are hands-down my favorite part of Databricks. I love being able to jump into a notebook with my team, tweak Spark SQL queries live on those massive shipment datasets, and watch everything sync instantly—without any version-control. It beats emailing notebooks back and forth or wrestling with merge conflicts; it feels like pair programming, but for data pipelines. And when you pair that with Delta Lake’s reliability for keeping my ETL jobs rock-solid on intermodal lane data, it ends up being a huge workflow saver. Top notebook perks for me are the real-time editing and sharing that keeps everyone aligned during debugging, the built-in version history that lets me roll back mistakes quickly, and the seamless Spark integration so I’m not constantly context-switching when doing big data transforms. **What do you dislike about Databricks?** One key drawback is the cost management—charges can accumulate rapidly if clusters are left running, requiring careful monitoring of DBU usage and auto-termination settings. Debugging intricate Spark job failures in notebooks often involves sifting through extensive log output, which extends resolution time considerably. Additionally, the UI experiences occasional performance delays under high workloads, impacting efficiency when responsiveness is essential. **What problems is Databricks solving and how is that benefiting you?** Databricks addresses core challenges in managing large-scale data processing, such as scalability limitations in traditional databases and the complexity of integrating disparate tools for ETL workflows. It enables distributed Spark processing across clusters to handle massive datasets efficiently, while Delta Lake provides ACID-compliant storage to ensure data integrity amid evolving schemas or concurrent updates. This benefits me by streamlining pipelines that feed BI tools, reducing processing times from days to hours and minimizing manual infrastructure oversight. Collaborative notebooks further enhance team productivity through real-time editing, eliminating version control issues and accelerating development cycles. **Official Response from Janelle Glover:** > We're glad to hear that you are enjoying the collaborative notebooks and the seamless Spark integration in Databricks. We understand your concerns about cost management and UI performance, and we are continuously working to improve these aspects for a better user experience. ### 39. Databricks Unifies Data and AI for Effortless ML at Scale **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User in Information Technology and Services | Mid-Market (51-1000 emp.) **Reviewed Date:** April 02, 2026 **What do you like best about Databricks?** What I like most about Databricks is how it brings data and AI into one place, so you’re not jumping between tools. It makes building and scaling ML models feel much more straightforward, especially with built-in experiment tracking. The integration with Apache Spark helps handle large datasets without extra setup. Overall, it just reduces the friction between raw data and actually getting useful AI outcomes. **What do you dislike about Databricks?** One thing I find challenging with Databricks is cost visibility-it can scale quickly, and predicting spend isn’t always straightforward. There’s also a bit of a learning curve, especially when working across notebooks, jobs, and cluster configs. And for simpler use cases, it can feel like overkill compared to lighter-weight solutions. **What problems is Databricks solving and how is that benefiting you?** Databricks solves the problem of fragmented data and AI workflows by bringing everything-data engineering, analytics, and ML-into one platform. It eliminates the need to move data across multiple systems, which reduces latency and pipeline complexity. For me, that means faster experimentation and smoother deployment of AI models without worrying about infrastructure. Overall, it helps focus more on solving business problems rather than managing tools. **Official Response from Janelle Glover:** > Thank you for sharing how Databricks has helped streamline your data and AI workflows, reducing latency and pipeline complexity. We're committed to providing a platform that allows users to focus on solving business problems rather than managing tools. ### 40. Efficient ETL and AI-Driven Data Validation **Rating:** 4.5/5.0 stars **Reviewed by:** Dinesh Kumar D. | Senior Data Analyst, Mid-Market (51-1000 emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I like the AI-supported environment in Databricks, which I use extensively for ETL tasks and experimental AIBD dashboards. It's really helpful for fixing code issues and handling logic implementation efficiently. The DLT feature is also a great addition for supporting streaming data. I find Delta Lake very useful for reliable data handling with its ACID transactions, schema enforcement, and reliable versioned data. Notebooks make it easy to develop, test, and debug data logic interactively. I also appreciate the workflows for automating and scheduling pipelines, which improve reliability and reduce manual effort. Databricks is cost-effective compared to other platforms like Synapse and Snowflake, and it's easy to track versions and handle failures. The initial setup was straightforward, with workspace creation and cluster setup being fairly easy for my team. **What do you dislike about Databricks?** The DLT, one of my personal experiences, as when set on DLT for one flow, I could not create another flow with the same table used previously. On a business aspect, it's normal to use one table for different reporting aspects as a base table and require different refresh timing. **What problems is Databricks solving and how is that benefiting you?** I perform ETL tasks and reporting with Databricks. It helps set up streaming data using DLT, and features like Delta Lake enhance data quality. Notebooks support interactive logic development, while workflows automate pipeline scheduling, reducing manual effort. **Official Response from Janelle Glover:** > Thank you for sharing your positive experience with Databricks. We're pleased to hear that the AI-supported environment, Delta Lake, and cost-effectiveness have been beneficial for your ETL tasks and reporting. We have noted your feedback about the limitations with Delta Live Tables and will take it into account for future improvements. ### 41. Streamlines ML Engineering with Powerful Features **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for machine learning engineering to basically solve all of the model life steps. I like using MLflow to track the entire model lineage and having the AI runtime assure that I have reproducible results. MLflow provides a structured, reproducible way to do my experiments and helps me keep track of what I've done before, and see how changes affect the overall metrics. The best thing about it is that it's open source, so I can also run it locally if I want to and inspect and export traces everywhere I want. **What do you dislike about Databricks?** I think environment management was always a bit of a hassle, so it's quite difficult to use Docker containers for that. Difficult thing was just finding out which were the best practices. There's some different ways to do the same thing. It was not clear which one is the best or preferred way to do it in our use cases. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for machine learning engineering to handle all model lifecycle steps. MLflow tracks model lineage, and the AI runtime ensures reproducible results. **Official Response from Jess Darnell:** > We're glad to hear that you find MLflow and AI runtime helpful for tracking model lineage and ensuring reproducible results. We understand that environment management can be challenging, and we are continuously working to improve our documentation and provide clear best practices for our users. ### 42. Powerful unified platform for data, analytics, and AI **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Consulting | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is that it brings data engineering, analytics, governance, and AI workflows into one unified platform. It makes it much easier to work with large datasets, collaborate across teams, and move from raw data to usable insights quickly. Features like Genie are especially useful because they make data more accessible through natural-language questions, helping business users explore data without always needing custom SQL or engineering support. **What do you dislike about Databricks?** The biggest downside is that Databricks can come with a learning curve, especially for people who are new to Spark, notebooks, clusters, or lakehouse architecture. Configuration and cost management also require attention, particularly as usage expands across multiple teams. Once the right patterns and governance are established, the platform becomes very powerful, but getting everyone onboarded and setting things up properly can take time. **What problems is Databricks solving and how is that benefiting you?** Databricks is solving the problem of fragmented data work: separate tools for data engineering, analytics, machine learning, governance, and business reporting. As someone working across structured data and data science, I benefit from having those workflows closer together in one lakehouse platform instead of constantly moving data between systems. It helps me build more reliable pipelines, explore large datasets faster, and support analytics or ML use cases from the same governed data foundation. Features like Genie also make it easier for non-technical stakeholders to ask questions of the data directly, which reduces bottlenecks and frees up technical teams to focus on higher-value modelling, architecture, and data quality work. The main benefit is speed and trust: faster time from raw data to insight, less duplication, fewer handoffs between tools, and better collaboration between engineering, analytics, and business teams. **Official Response from Jess Darnell:** > We're thrilled to hear that Databricks has helped you solve the problem of fragmented data work and brought your data engineering, analytics, and AI workflows closer together. We understand the importance of streamlining these processes and are committed to providing a platform that supports reliable pipelines, faster data exploration, and collaboration across technical and non-technical teams. ### 43. Seamless FinOps with Databricks: Innovative, Efficient, and Reliable **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like the Databricks workflows and I'm excited about getting started with the lake-based features, which I think are excellent. We also partner with Genie, which is an AI feature that helps us become more efficient. I appreciate that there are out-of-the-box features that help us fast-track our processes and automate certain tasks using simple language. The initial setup of Databricks was pretty easy. We had some Databricks SMEs who got everything working for our environment quite smoothly, making the adoption process easy. Overall, Databricks is serving the purpose it was taken for, and we're receiving everything we need from it without anything that I would want changed. **What do you dislike about Databricks?** None **What problems is Databricks solving and how is that benefiting you?** I use Databricks for FinOps, cloud cost management, and as a hub for finance data. It efficiently processes our finance data, creating visualizations and dashboards that aid leadership. With Genie and out-of-the-box features, it automates tasks, fast-tracks processes, and formats data for downstream needs. **Official Response from Janelle Glover:** > We're thrilled to hear that you're enjoying the Databricks workflows, Lakebase features, and Genie, as well as the seamless setup process. It's great to know that Databricks is serving your needs efficiently. Thank you for sharing your positive experience! ### 44. Databricks Streamlines Data Workflows with Powerful, Scalable Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Transportation/Trucking/Railroad | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks is a great platform that makes it easy to work with large amounts of data. It provides a user-friendly experience and offers powerful tools for data engineering, analytics, and machine learning. The platform is reliable, scalable, and helps teams collaborate effectively. Overall, Databricks has been a valuable solution for improving productivity and streamlining data workflows. **What do you dislike about Databricks?** One downside of Databricks is that it can be expensive, especially as usage grows. Some advanced features may also have a learning curve for new users. Additionally, managing costs and understanding the pricing structure can sometimes be challenging. However, the overall capabilities and performance of the platform generally outweigh these drawbacks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to work with data and run analytics workloads more efficiently. The platform helps simplify data management and provides a scalable environment for processing large datasets. It has helped improve workflow efficiency, collaboration, and overall productivity while making it easier to access and analyze data. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been valuable in streamlining your data workflows and improving productivity. Our platform is designed to provide a user-friendly experience and powerful tools for data engineering, analytics, and machine learning. ### 45. Boost Data Management with Speed and Agility **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks because it helps us a lot to centralize information and ensure the quality of our data. I am impressed by the speed at which technology moves in Databricks, that they listen to customers and try to support us. The integration with Genie has been crucial for us, as it helps us a lot in various use cases. Managing tokens was a repetitive task for my team, but with Databricks we can delegate and look for other execution alternatives. Additionally, we decided to switch to Databricks because this technology evolves quite a bit. **What do you dislike about Databricks?** The topic of agent governance becomes crucial; often maintaining a governance of agents or tokens per project is something we lack and that would greatly help to establish. At the beginning, like everything, it was complicated but with guidance we can stabilize, especially the technological change was what made it difficult in the organization. **What problems is Databricks solving and how is that benefiting you?** Databricks helps me centralize information and ensure data quality. It facilitates token management, allowing us to delegate repetitive tasks, which gives us room to explore other options. **Official Response from Janelle Glover:** > We're glad to hear that Databricks has been able to centralize your information and ensure data quality. We appreciate your feedback on the speed of technology and the integration with Genie. We understand the importance of agent governance and are continuously working to improve in this area. ### 46. Unified Platform with Smooth Integration **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I really value how smooth and integrated Databricks is for auto job scheduling, making it easy to share work with colleagues. It's convenient to have everything under one umbrella, so I don't need to rely on multiple tools to integrate jobs into the right format and tables. I appreciate that you can easily connect repos to read code and get notifications. Databricks provides a single platform where I can execute procedures and choose the kind of machine to run my tasks. The setup was not difficult, taking about a week or two to get used to, and it's quite easy once you understand the data and different product offerings. Overall, Databricks makes processing all our customers' data, transformations, and running data science models straightforward. **What do you dislike about Databricks?** I think the Genie was something which I was feeling like it was not performing that well because whenever we're asking it to write a code, it was taking a longer time. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to process and transform customer data, run data science models, and perform auto job scheduling. It integrates smoothly under one platform, eliminating reliance on multiple tools, and simplifies sharing work with colleagues. **Official Response from Janelle Glover:** > We appreciate your feedback on your experience with Databricks. We're sorry to hear about the challenges you faced with Genie and will take this into consideration for future improvements. ### 47. Databricks’ Unified Platform Simplifies Scalable Data Pipelines and Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Verified User in Banking | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like best about Databricks is its unified platform that brings together data engineering, analytics, AI, and governance in a single environment. It simplifies building reliable data pipelines, supports scalable processing with Spark and Delta Lake, and enables teams to collaborate more effectively across the entire data lifecycle. **What do you dislike about Databricks?** The quality and depth of some training and learning materials could be improved. In some cases, the content feels too high-level and would benefit from more real-world examples, hands-on exercises, and deeper technical explanations for advanced users. **What problems is Databricks solving and how is that benefiting you?** Databricks helps solve the challenge of managing large-scale data processing, analytics, and AI workloads across multiple tools and platforms. By providing a unified environment for data engineering, data warehousing, governance, and machine learning, it reduces complexity and improves productivity. For me, this means I can build and maintain data pipelines more efficiently, ensure data reliability with Delta Lake, and spend more time delivering business value instead of managing infrastructure. **Official Response from Aunalisa Arellano:** > We're glad to hear that you appreciate the unified platform and its ability to simplify data pipelines and collaboration. We're continuously working to improve our training and learning materials to provide more practical and in-depth content for all users. Thank you for the feedback and for taking the time to leave a review! ### 48. Versatile and Efficient, Yet a Steep Learning Curve **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I use Databricks for ETL in both my personal projects and my company. My favorite feature is the Genie AI assistant. Databricks solved a challenging task for my team in the gas and energy sector by helping us get the most out of our KPIs. It provided preprocessing techniques and job pipelines that made it possible to write raw records to the delta tables, even when the data was riddled with noise. I like how Databricks is a one-stop shop for any on-premises cloud provider. It's very versatile and offers different services that work with Azure and AWS, yet it also has its own data lake service. **What do you dislike about Databricks?** I think the official documentation can use a bit more beginner-friendly information. The way I learned about it was trial and error and word-of-mouth type of information. It was a bit tricky on my end because I’ve never really worked along the lines of data science or data engineering. Interfaces were similar to what I knew, but it was more about how much I knew about the product. **What problems is Databricks solving and how is that benefiting you?** Databricks helps my team in the gas and energy sector by providing preprocessing techniques and job pipelines to extract valuable information from noisy data. It's a versatile one-stop shop for cloud providers like Azure and AWS, offering a data lake service. **Official Response from Jess Darnell:** > Thank you for sharing your experience with Databricks! We're glad to hear that the Genie AI assistant and the versatile services have been beneficial for your ETL projects. ### 49. Scalable, Unified, and Easy to Use, But Needs Dashboard Improvements **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User | Mid-Market (51-1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** I like Databricks for several reasons. Its scalability enables faster onboarding of multiple data sources within the same framework. The unified platform is fantastic for collaboration across our analytics, business, and data scientist teams. I appreciate the performance and the governance features it offers. The AI enablement is quite impressive, as it reduces development work and allows businesses to get faster insights using simple plain English with tools like genie. Overall, the combination of these features makes Databricks a valuable enterprise data and AI platform. **What do you dislike about Databricks?** Data governance, it is not having business glossary, databricks dashboard could be better. Two area of improvements are richer in the business glossary experience within the unity catalog and more advanced dashboard capability for business users especially when compared to specialised BI tools like tableau, PowerBI etc. **What problems is Databricks solving and how is that benefiting you?** Databricks helps unify and transform data from various sources, performing robust quality checks and governance. Its scalability aids in faster onboarding, and the unified platform enhances collaboration. AI enablement reduces development work, speeds up insights, and the scalability improves overall performance. **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks scalable, unified, and easy to use, and that you're benefitting from using Genie. We appreciate your feedback on the need for dashboard improvements, and we'll definitely consider that for future updates. ### 50. Empowers Collaborative Data Science with Minor Learning Curve **Rating:** 4.5/5.0 stars **Reviewed by:** Arman M. | Software Developer, Computer Software, Small-Business (50 or fewer emp.) **Reviewed Date:** April 01, 2026 **What do you like best about Databricks?** I use Databricks for a lot of things. The main ones are making sense out of the data, looking at chunks of data, and doing machine learning. Databricks makes these tasks very easy and helpful, especially for data projects. It's great for collaborating with friends and developing my Python code in notebooks. I like Databricks because it has good capabilities for handling big data and is excellent for working with the data and machine learning. It's also easy to use when working with people, as many can work on a project and share their findings. **What do you dislike about Databricks?** Databricks is very powerful, but there are some things that need improvement. It's hard to learn for beginners when working with Spark and setting up clusters, as this was confusing at first. Sometimes the interface and settings can feel complicated. I think it would be helpful if there were clear setup instructions so new users could get started easily with Databricks. **What problems is Databricks solving and how is that benefiting you?** I use Databricks to make sense of data, collaborate with others, and develop Python code. It simplifies data engineering, machine learning, and handling data while allowing multiple people to work on notebooks simultaneously. **Official Response from Janelle Glover:** > Thank you for sharing your positive experiences with Databricks, including its capabilities for handling big data and machine learning. We understand the challenges of learning to work with Spark and setting up clusters, and we are committed to enhancing our resources to make the onboarding process smoother for beginners. ## 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, 1 upvote - [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?page=3&qs=pros-and-cons§ion=pricing&secure%5Bexpires_at%5D=2026-07-22+13%3A30%3A26+-0500&secure%5Bsession_id%5D=214e5c5a-0dac-450d-bca4-844da9e7acc8&secure%5Btoken%5D=e38ce60d2f18bfc44fff96687b76d7e5c5b6c5aecf77e05134411fdd7c856fb7&format=llm_user) ## Databricks Integrations - [Agentforce Sales (formerly Salesforce Sales Cloud)](https://www.g2.com/products/agentforce-sales-formerly-salesforce-sales-cloud/reviews) - [Amazon Redshift](https://www.g2.com/products/amazon-redshift/reviews) - [Amazon Relational Database Service (RDS)](https://www.g2.com/products/amazon-relational-database-service-rds/reviews) - [Anaplan](https://www.g2.com/products/anaplan/reviews) - [Apache Airflow](https://www.g2.com/products/apache-airflow/reviews) - [Apache Kafka](https://www.g2.com/products/apache-kafka/reviews) - [AWS Glue](https://www.g2.com/products/aws-glue/reviews) - [AWS Lambda](https://www.g2.com/products/aws-lambda/reviews) - [Azure Databricks](https://www.g2.com/products/azure-databricks/reviews) - [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 DevOps Server](https://www.g2.com/products/azure-devops-server/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) - [Base SAS](https://www.g2.com/products/base-sas/reviews) - [Claude](https://www.g2.com/products/claude-2025-12-11/reviews) - [Claude Code](https://www.g2.com/products/anthropic-claude-code/reviews) - [Crunchbase](https://www.g2.com/products/crunchbase/reviews) - [Dash](https://www.g2.com/products/dash-for-brands-ltd-dash/reviews) - [Datadog](https://www.g2.com/products/datadog/reviews) - [dbt](https://www.g2.com/products/dbt/reviews) - [DigitalOcean](https://www.g2.com/products/digitalocean/reviews) - [Domo](https://www.g2.com/products/domo/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) - [GitLab](https://www.g2.com/products/gitlab/reviews) - [Google Analytics](https://www.g2.com/products/google-analytics/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) - [Microsoft Copilot Studio](https://www.g2.com/products/microsoft-microsoft-copilot-studio/reviews) - [Microsoft Excel](https://www.g2.com/products/microsoft-excel/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) - [MLflow](https://www.g2.com/products/mlflow-mlflow/reviews) - [MySQL](https://www.g2.com/products/mysql/reviews) - [ObjectWay SpA](https://www.g2.com/products/objectway-spa/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) - [Prophecy](https://www.g2.com/products/prophecy-prophecy/reviews) - [Salesforce Agentforce](https://www.g2.com/products/salesforce-agentforce/reviews) - [Salesforce Headless 360 Platform (formerly Salesforce Platform)](https://www.g2.com/products/agentforce-360-platform-formerly-salesforce-platform/reviews) - [SAP Ariba](https://www.g2.com/products/sap-ariba/reviews) - [SAP ECC](https://www.g2.com/products/sap-ecc/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) - [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/apache-spark/reviews) - [Spark SQL](https://www.g2.com/products/spark-sql/reviews) - [Spotfire Analytics](https://www.g2.com/products/spotfire-analytics/reviews) - [Tableau](https://www.g2.com/products/tableau/reviews) - [Visual Studio Code](https://www.g2.com/products/visual-studio-code/reviews) - [Workday HCM](https://www.g2.com/products/workday-hcm/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](https://www.g2.com/products/cloudera/reviews) - 4.1/5.0 (131 reviews) - [Snowflake](https://www.g2.com/products/snowflake/reviews) - 4.5/5.0 (708 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (356 reviews)