--- title: Databricks Reviews meta_title: 'Databricks Reviews 2026: Details, Pricing, & Features | G2' meta_description: Filter 1363 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: 1363 scale: '5' date_modified: '2026-08-07' 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:** 1,363 ## About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics, and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, 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. Managed Spark Clusters and Collaborative Notebooks That Just Work **Rating:** 4.5/5.0 stars **Reviewed by:** Ranjit P. | Cloud Engineer, Information Technology and Services, Mid-Market (51-1000 emp.) **Reviewed Date:** June 03, 2026 **What do you like best about Databricks?** The best thing about Databricks is the managed Spark clusters. Earlier, setting up Apache Spark manually on AWS or Azure was a big headache. Now, with Databricks, I can spin up a cluster with just a few clicks. The auto-scaling feature works very well, when processing heavy data workloads, it automatically adds nodes and reduces them when done, which saves some cloud costs. Also, the collaborative notebooks are amazing. My team members and I can work on the same Python or SQL code at the same time, just like Google Docs. The integration with Delta Lake is also a big plus because it gives ACID transactions directly on cloud storage, so data corruption issues are very rare now. **What do you dislike about Databricks?** The biggest issue is the pricing. Databricks DBUs Databricks Units are quite expensive, and if you are not careful with cluster configurations or leave a cluster running by mistake, the cloud bill will jump very high quickly. The cost management tools inside the platform could be much better. **What problems is Databricks solving and how is that benefiting you?** We are solving the big problem of data silo and slow ETL Extract, Transform, Load pipelines. Before Databricks, our data science team and data engineering team were working in different environments, and moving data between them was painful. Now, Databricks acts as a single Unified Analytics Platform. We ingest raw data into Azure/AWS, clean it using Spark SQL, and the machine learning guys use the same platform to train models. It has reduced our data processing time from hours to minutes, which helps us deliver client projects much faster. **Official Response from Aunalisa Arellano:** > It's great to hear that Databricks has helped to solve the challenges of data silos and slow ETL pipelines for your team. We are committed to providing a unified analytics platform that enables seamless collaboration and faster data processing for our users. ### 2. Comprehensive Ecosystem, Complex Setup **Rating:** 5.0/5.0 stars **Reviewed by:** Eleazar C. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** What I like the most about Databricks is the whole ecosystem. It's not easy to have everything you need in a single platform that already has access to the data by its nature. You don't have to handle complex integrations for new projects like data engineering, machine learning, creating dashboards, or developing applications. **What do you dislike about Databricks?** I think Databricks can improve in the complexity. It gets difficult or tricky because there are plenty of things and features, and at some point, it becomes complicated to catch all of them. The user experience can improve, especially for stakeholders that are not 100% technical. It's not easy to set up; you need to set up a lot of things, and when you just start, it's really complicated to get things done. **What problems is Databricks solving and how is that benefiting you?** Databricks integrates different system sources, manages high data volumes with distributed computation, and keeps storage costs low. **Official Response from Jess Darnell:** > We appreciate your feedback on the complexity of Databricks. We are constantly working to improve the platform and make it more user-friendly, especially for those who are not fully technical. Thank you for bringing this to our attention. ### 3. Prominent when scaling LLMs and pipelines, but be mindful of the cloud bill! **Rating:** 4.5/5.0 stars **Reviewed by:** Anupama J. | Junior Data Analyst, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** As a researcher of AI, it seems like infrastructure is the number one problem, especially setting up clusters, building drivers, and scaling distributed training. Databricks takes care of all that by itself. I can easily and quickly deploy a cluster of nodes for GPUs with PyTorch and DeepSpeed preconfigured in a few clicks. This built-in MLflow is a lifesaver to keep track of experiments. All the hyperparameters or architecture changes with respect to an embedding model are automatically being tracked every time. ESSENTIAL: I no longer have to struggle to get clean and versioned datasets from data engineers for training purposes when working with Delta Lake. Getting around those feature stores is also very easy with the Unity Catalog. **What do you dislike about Databricks?** First, it's really expensive, brother. On an extremely large A100 GPU cluster, if you, or someone on your team, forget to configure the auto-terminate, you are going to have a very bleak day with finance tomorrow. Expenses can add up quickly. Additionally, although they are too lightweight to be an ideal platform for distributed deep learning, the debugging workflow may be tedious. The intersection of the computing nodes makes it difficult to find the exact PyTorch-Out-Of-Memory or CUDA-Out-Of-Memory error occurring in the Spark logs. I also feel like the native MLflow UI in Databricks isn't as advanced and specialized as some of the tools like Weights & Biases. **What problems is Databricks solving and how is that benefiting you?** It fills the oceanic yawning void between research in AI and data engineering. To get terabytes of unstructured text data pre-trained in the past was a multi-step nightmare in different environments. I can make heavy data preparation using Spark and immediately switch to Python for training my model in the same ecosystem. It helps to communicate goodwill amongst the entire team. Everything is in one workspace, so my transition of raw data to experiment tracking to finally registering the model in the registry is done in one, unified, pipeline. **Official Response from Jess Darnell:** > Thank you for your feedback - we appreciate your review! ### 4. Databricks is super fast with big data, yet slow to learn. **Rating:** 4.0/5.0 stars **Reviewed by:** Khushi S. | Data Analyst Intern, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** I work as a Data Analyst and every day, I use Databricks to complete my data tasks. The best thing I like is the processing speed. We were loading large tables and it was taking too long before we could load big tables using normal databases. My rich SQL queries are very fast in Databricks due to the use of Apache Spark backend. In addition, the Notebook feature is quite useful to me. I can create SQL code in a cell and in the next cell, I can write Python or Pandas code to do some particular data cleaning. It is also easy to connect Databricks to our Power BI dashboards. **What do you dislike about Databricks?** There are some things which I am facing issues with. First is the cluster starting time. In the morning, it takes 5-10 minutes to boot but once I log in. When the management requests urgent report, then I must make myself sit and wait till the cluster turns green. **What problems is Databricks solving and how is that benefiting you?** Its primary issue that it is resolving is the ability to process large volumes of company data without system freezing. Previously, it was a pain to deal with millions of rows. I can now easily query, filter and aggregate large datasets. It is helping my team since data engineers and data analysts are sharing the same workspace. In case data engineers make a new table, I can see it right away and can query it directly in my notebook. Using notebooks to share with other team members to have it reviewed is similar to using Google Doc, which makes my everyday reporting work incredibly quick. **Official Response from Jess Darnell:** > We appreciate your feedback on the benefits of using Databricks for processing large datasets and the seamless collaboration between data engineers and data analysts. We acknowledge the issue with cluster starting time and will strive to enhance the performance in this area. ### 5. Best tool to work with big dealer data, but requires technical team. **Rating:** 4.5/5.0 stars **Reviewed by:** Dilkash N. | Assistant Sales Manager (Institutional Sales), Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** We have a very big dealer and distributor network throughout India in our sanitaryware business. Big sales data are being generated everyday. Speed is my favorite thing about Databricks. Whenever we use simple excel or old software before, it is always hanging. And now our company data team is churning through the millions of rows in a short time. As a Senior Sales Specialist, I am making sure that I get my territory dashboard and forecasting reports at least daily in the morning. It is bringing all disperse data together in a good manner. **What do you dislike about Databricks?** Worst thing is that it is highly technical software. As a sales person, I cannot apply it in locating data directly. I need to request data engineering or IT team to code or make query every time I desire some new custom report. User-non technical interface is becoming very complicated. And my management is continually saying this costs a great deal. **What problems is Databricks solving and how is that benefiting you?** We are solving target tracking and inventory matching problem. We have a wide range of products, such as tiles, faucets, and washbasins, in Cera. Databricks is assisting our company to understand what region is selling what product more and the trend in the market. My advantage is due to this, because I can advise my local dealers accordingly, as to next month order. It is providing highly precise sales forecast and saving me my manual reporting time and I am closing my sales targets with ease. **Official Response from Jess Darnell:** > We're glad to hear that Databricks is helping you with speed and data consolidation. We understand the challenges of technical complexity and will continue to work on improving the user interface for non-technical users. ### 6. Databricks Unifies Data Engineering, Analytics, and ML for Faster Collaboration **Rating:** 4.0/5.0 stars **Reviewed by:** Rudi T. | Cloud Platform Engineer, Enterprise (> 1000 emp.) **Reviewed Date:** June 02, 2026 **What do you like best about Databricks?** What I like most is how Databricks brings data engineering, analytics, and machine learning together in one environment. Our teams no longer need to jump between multiple tools to build pipelines, analyze data, and train models, which keeps work more consistent and streamlined. The notebook experience is genuinely collaborative and helps us move from exploration to development much faster. Integration with Spark and Delta Lake also makes it easier for us to process large datasets efficiently and stay organized as projects grow. **What do you dislike about Databricks?** The platform can feel overwhelming for new users due to the sheer number of features available. Some of the more advanced configurations also require a solid understanding of cloud infrastructure and cluster management, which can add to the learning curve. Cost monitoring needs close attention as well, especially for teams that run large workloads on a frequent basis. **What problems is Databricks solving and how is that benefiting you?** Databricks has helped us modernize our data platform and replace several disconnected tools. We now use it as a single place for ETL processing, analytics, and machine learning workloads. As a result, our operational complexity has gone down, and collaboration between data engineers and analysts has improved. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks' unified environment and collaborative notebook experience helpful for your teams. We understand that the platform may feel overwhelming for new users, and we're continuously working on improving the user experience and providing more resources for learning and support. ### 7. Streamlined Data Integration with Robust Collaboration **Rating:** 5.0/5.0 stars **Reviewed by:** Samuel D. | Small-Business (50 or fewer emp.) **Reviewed Date:** June 17, 2026 **What do you like best about Databricks?** I like Databricks for its centralized UI that allows for seamless development, collaboration, and deployments. I appreciate the Unity Catalog Governance, which helps with data sharing in a controlled yet federated manner. The platform's capability for seamless data import, quick retrofit pipelines, and automation makes my tasks more efficient. Additionally, bringing disparate data sources together and automating with notebooks to rewrite ETL processes was easy, and it quickly sped up the onboarding of legacy ML models. **What do you dislike about Databricks?** DABs are limited to notebooks. I want it for DLTs, ML models, Genie. GitHub integration is confusing with deployment handoffs and collaboration. **What problems is Databricks solving and how is that benefiting you?** I use Databricks for reporting and ML models, solving real-time analytics and time to value. The centralized UI aids in development and collaboration, while Unity Catalog helps with data sharing. It's easy to bring disparate data sources and automate with notebooks for ETL. **Official Response from Aunalisa Arellano:** > We're glad to hear that you find Databricks' centralized UI and Unity Catalog Governance helpful for seamless development and data sharing. We appreciate your feedback on the limitations with DABs and GitHub integration, and we'll take that into consideration for future improvements. ### 8. A Game Changer for Unifying Data Engineering and ML, but Watch Your Compute Costs **Rating:** 5.0/5.0 stars **Reviewed by:** Lokesh S. | Senior Data Scientist, Mid-Market (51-1000 emp.) **Reviewed Date:** May 28, 2026 **What do you like best about Databricks?** As a Senior Data Scientist at a mid-sized tech company, I've used Databricks for the last couple of years, and it has really transformed our data teams. The main use case we want to process large amounts of user interaction data to create predictive models, namely customer churn, recommendation engines, and customer lifecycle value (LCV) estimation. Prior to Databricks, our workflows were very disjointed. The data engineers utilized one set of complicated tools for ETL tasks and the data scientific research group utilized completely various neighborhood environments for modelling. Databricks gave everyone a common platform and workspace, a cloud-based experience, which put everyone together under one roof.I like the ability to work with collaborative notebooks together with great computing at the same time the most. It's a significant productivity win to be able to code in Python, SQL and Scala — and, vitally, do so in the same environment as the data engineers who are creating the core pipelines. Additionally, I find the out-of-the-box integration with MLflow to be a game-changer in my workday routine. It eliminates the pain of managing version registries, tuning parameters, and more complicated model experiments. I have to also point out the ease with which they have improved cluster management. As a data scientist, I need to use a heavy machine learning model, one that is not always in use for the duration. I can start a distributed, powerful Spark cluster in a few clicks, train my model on it, and then quickly configure it to automatically kill itself after the job completes - I do not want to waste resources. **What do you dislike about Databricks?** The platform doesn't have exceptional user-friendliness, though, and there are some drawbacks that you'll need to navigate with care. The greatest disadvantage is the loss of control of spending if you're not careful. With so much of the complicated back-end infrastructure abstracted away, it's quite easy for a newer team member to provision an unnecessarily large compute cluster or forget to switch on auto-termination, with a very unpleasant surprise on the monthly billing statement. One must be careful about creating rigid rules for use of the workplace and tracking how it is used. Moreover, it can be unpredictable to learn the learning curve of a distributed computing paradigm that is different from the one analysts or data scientists already have experience with, such as Apache Spark. They have come a long way in introducing features that are similar to the standard Python library but for complex distributed errors, a lot of knowledge about the inner workings is still needed for debugging. The user interface can also sometimes be a bit slow and cumbersome when working with deep levels of workspace folders in which there are hundreds of notebooks from the legacy version. **What problems is Databricks solving and how is that benefiting you?** As for real-world problems solved, Databricks did away with that well-known situation of having a predictive model I love on my laptop, but not at all in the production environment. We standardized our runtime environments throughout the entire organization and set up MLflow for deployment, saving us many painful weeks to only a couple of days for our models to go to production. Yet another huge success for our company is that we removed the silos among our departments. We do not now throw a clean data table over a metaphorical wall for me to analyse second thoughts because I am not a data engineer. When I recently had to work with a complex and custom feature that was created for an algorithm that recommends products for users, I wrote this feature with the data engineering lead in a common notebook on Databricks, and we tested and optimized this pipeline together. Historically, we would have deployed it the wrong way the first time, but that wouldn't have worked with our previous infrastructure! It has really made our team a very effective cross functional team! **Official Response from Jess Darnell:** > Thank you for sharing your experience with Databricks! We're glad to hear that it has transformed your data teams and provided a common platform for collaboration. We appreciate your feedback on the user-friendliness and cost control, and we are continuously working to improve in these areas. It's great to hear that Databricks has helped standardize runtime environments and removed silos among departments, leading to more efficient cross-functional teamwork. ### 9. Powerful Platform with Easy Data Migration C **Rating:** 4.5/5.0 stars **Reviewed by:** Nitin A. | Enterprise (> 1000 emp.) **Reviewed Date:** June 16, 2026 **What do you like best about Databricks?** Databricks provides a unified platform for data engineering, analytics, and AI, reducing migration complexity. * It supports seamless integration with cloud storage, databases, and legacy data platforms. * Built-in scalability allows organizations to migrate from on-premises or traditional data warehouses without major infrastructure changes. * With automated optimization and open formats like Delta Lake, data migration becomes faster, more reliable, and future-proof. **What do you dislike about Databricks?** The interface could be better and The platform can have a steep learning curve for new users, especially when managing clusters, jobs, and workspace administration. * Cost management could be more transparent, as compute and storage expenses can grow quickly without proper monitoring. * Debugging and troubleshooting distributed workloads can sometimes be challenging compared to traditional environments. * Some enterprise features and integrations require additional configuration, which can increase setup and operational complexity. **What problems is Databricks solving and how is that benefiting you?** Replacement of synapse pain points **Official Response from Janelle Glover:** > We're glad to hear that you find Databricks to be a powerful platform for data engineering, analytics, and AI, with seamless integration and built-in scalability for data migration. We understand your concerns about the interface, learning curve, and cost management. We are constantly working to improve the user experience and provide more transparent cost monitoring. ### 10. 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. ### 11. 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. ### 12. 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. ### 13. 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. ### 14. Comprehensive AI Platform with Unified Governance **Rating:** 5.0/5.0 stars **Reviewed by:** Verified User | Enterprise (> 1000 emp.) **Reviewed Date:** August 06, 2026 **What do you like best about Databricks?** I use Databricks as the backbone for all my data and AI work. I love that everything is in a single place and all connects, which allows me to manage everything from raw data digestion to deploying AI products. Unity Catalog provides a single governance source of truth with permissions throughout the platform, which is incredibly useful. Genie Spaces offer an interface in natural language for non-technical stakeholders, saving me time as I don't need to create additional Tableau reports. I can ship everything as code rather than clicking through notebooks manually, which lets me scale much faster. Databricks is a comprehensive platform and is ahead of the curve when it comes to artificial intelligence. **What do you dislike about Databricks?** The biggest issue I have is that I'm not applied for admin so I don't have visibility into a lot of my costs or access to control more things in the platform. I also wish there was a way in Genie code to make it more clear which aspects are within my control and which are not, and to have an integrated system to notify my platform admin when there are issues. **What problems is Databricks solving and how is that benefiting you?** I use Databricks as the backbone for all my data and AI work. It's an all-in-one platform, eliminating the need for handoffs between different tools. Unity Catalog gives me a single governance source, ensuring consistency and saving my time. ### 15. 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. ### 16. 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. ### 17. 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. ### 18. 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. ### 19. Feature-Rich, Intuitive UI with Great AI Assistance and Easy Integrations **Rating:** 4.5/5.0 stars **Reviewed by:** Verified User in Gambling & Casinos | Mid-Market (51-1000 emp.) **Reviewed Date:** July 29, 2026 **What do you like best about Databricks?** many features with intuitive ui, great ai assistance and you easily integrate with other services. I like the facts that yo can orchestrate jobs and notebooks easily, run queries on large volumes of data and the fact that the AI genie can help you a lot understand and implement better and faster tasks. **What do you dislike about Databricks?** sometimes the documentation is not easily obtainable. I have also come across cases where the catalogue quick search did not yield my table, but the table existed. generally I am happy **What problems is Databricks solving and how is that benefiting you?** doing large scale queries, making it fast and easy to generate reports and dashboards for both technical and non technical people. bridges the gap between tech and product/business **Official Response from Jess Darnell:** > We're glad to hear that you are enjoying the feature-rich and intuitive UI, as well as the AI assistance and easy integrations. We appreciate your feedback and will take your comments about documentation and catalogue search into consideration for future improvements. ### 20. 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. ### 21. 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! ### 22. Easy API Data Pulls and Collection Management, Plus AI-Powered Coding **Rating:** 4.0/5.0 stars **Reviewed by:** Reetika P. | Quality engineer, Mid-Market (51-1000 emp.) **Reviewed Date:** July 28, 2026 **What do you like best about Databricks?** It easily pulls data from the API, and within the same dataset we can manage our collections. We also have the option to write code using the AI. **What do you dislike about Databricks?** In our current setup, BigQuery SQL queries run with predictable costs that are easy to control. With Databricks, though, if a data engineer spins up an oversized cluster or leaves a node running after processing dealer posts or telematics logs, compute costs can ramp up quickly and may go unnoticed. **What problems is Databricks solving and how is that benefiting you?** For our projects, we use it to pull the source, or raw, data from the APIs and then transfer that same data into BigQuery. It essentially acts as a middleman for us. **Official Response from Jess Darnell:** > We're glad to hear that you find Databricks useful for pulling data from APIs and managing collections within the same dataset. The AI-powered coding feature is also a great benefit. We understand your concerns about potential cost escalation with Databricks. We recommend closely monitoring cluster usage and setting up alerts to avoid unexpected compute costs. ### 23. 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. ### 24. 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. ### 25. 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. ### 26. 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. ### 27. 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. ### 28. 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. ### 29. 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. ### 30. 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. ### 31. 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. ### 32. 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. ### 33. 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. ### 34. 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. ### 35. 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. ### 36. 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. ### 37. 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! ### 38. 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. ### 39. 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. ### 40. 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. ### 41. 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. ### 42. 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. ### 43. 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! ### 44. 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. ### 45. 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. ### 46. 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. ### 47. 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. ### 48. 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. ### 49. 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. ### 50. 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. ## 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=2026-08-07+17%3A22%3A05+-0500&secure%5Bsession_id%5D=86cdbffb-9cf3-4429-a1e5-5381aa09a90c&secure%5Btoken%5D=40c3707dd178e65ee28090cce35854603b708b38622d5993fea0f825cbeb1aa2&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) - [Amazon S3 Glacier](https://www.g2.com/products/amazon-s3-glacier/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) - [Dataiku](https://www.g2.com/products/dataiku/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 BigQuery](https://www.g2.com/products/google-cloud-bigquery/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) - [Unity Catalog Command Center](https://www.g2.com/products/unity-catalog-command-center/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 - Customizable Reports - Marketing Reports - Sales Reports - Activity Dashboard - Interactive Reports - Customizable Reports - Customizable Reports - Activity Dashboard - Customizable Dashboard **Additional Functionality** - Continuous Integration - Drag & Drop - Backup and Recovery - Multiple Programming Languages Supported - Continuous Deployment - Code Development - For No-Code Development - Version Control - Configurable Workflow - Graphical User Interface - For Low-Code Development - Activity Dashboard - Generative AI - UI Prototyping - Source Control - Software Development - API - Data Import/Export - Web App Development - Custom Development - Code Repository Integration - Data Security - Mobile Development - Access Controls/Permissions - Game Development - Collaboration Tools - Application Security - Alerts/Notifications - Automated Testing - Integrated Development Environment - Offline Access - Debugging - AI Copilot - Reporting/Analytics - Compatibility Testing - Pre-built Templates - Third-Party Integrations - Data Modeling - Customizable Branding - Data Visualization - Code Editing **Additional Functionality** - Tagging - Natural Language Processing - Data Extraction - Multi-Language - Predictive Analytics - Drag & Drop - Speech Recognition - Reporting/Analytics - Data Storage Management - Virtual Personal Assistant (VPA) - AI Copilot - Customer Segmentation - Collaboration Tools - Data Import/Export - Generative AI - For eCommerce - Role-Based Permissions - Customizable Branding - Search/Filter - Monitoring - Document Management - API - Data Visualization - Trend Analysis - Machine Learning - Access Controls/Permissions - Alerts/Escalation - Performance Metrics - Real-Time Data - Third-Party Integrations - Mobile App - Multiple Data Sources - For Sales Teams/Organizations - Sentiment Analysis - Activity Dashboard - Chatbot - Workflow Automation **Additional Functionality** - Data Cleansing - Data Extraction - Third-Party Integrations - Data Verification - Information Governance - Data Capture and Transfer - AI Copilot - Customizable Reports - Data Migration - Behavior Analytics - Real-Time Reporting - Interactive queries - Audit Trail - Multiple Data Sources - Document Storage - Access Controls/Permissions - User Management - AI/Machine Learning - Master Data Management - Task Scheduling - Data Import/Export - Monitoring - Customer Database - Audit Management - Reporting/Analytics - Data Visualization - Collaboration Tools - Workflow Management - Full Text Search - Compliance Management - Generative AI - Data Quality Control - Data Connectors - Automatic Backup - Activity Tracking - Activity Dashboard - Data Security - Data Analysis Tools - Data Synchronization - Metadata Management - Dashboard Creation - Visual Analytics - Secure Data Storage - Data Integration - Database Support **Additional Functionality** - Code Generation - Text to Image - Generative AI - API - Natural Language Processing - Virtual Characters and Avatars - Content Generation - Personalization and Recommendation - Conditional Generation - Transformer Model - Automated Image & Video Editing - Interactive and Co-Creative Systems - Text Summarization - Data Augmentation - Variation Autoencoder Models - Adversarial Training - Transfer Learning and Fine-tuning - Simulation and Scenario Generation - Creative Design - AI Copilot - Prompt Engineering - Foundation Model **Administration** - Data Modelling - Recommendations - Workflow Management - Dashboards and Visualizations - Configurable Workflow - Rules-Based Workflow **Management** - Reporting - Auditing **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **System** - Data Ingestion & Wrangling - Real-Time Data **Data Preparation** - Connectors - Data Governance **Data Management** - Data Integration - Data Compression - Data Quality - Built-In Data Analytics - In-Database Machine Learning - Data Lake Analytics - ETL - Extract Transfer Load - Data Capture and Transfer - Real-Time Analytics - Reporting/Analytics **Management** - Business Glossary - Data Discovery - Data Profililng - Reporting and Visualization - Data Lineage - Data Visualization **Deployment** - Language Flexibility - Framework Flexibility - Versioning - Ease of Deployment - Scalability **Reports** - Reports Interface - Share Reports - Steps to Answer **Data Management** - Data Integration - Metadata - Self-service - Automated workflows **Functionality** - Ease of Use - File Management - Multi-Language Support - Customization - Straight-Out-the-Box Functionality - Help Guides - Patching & Updates - Workflow Management - Change Management - Deployment Management - Performance Management - Compliance Management - Lifecycle Management - Task Management - Document Management - Database Support **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - API Security - Data Security - Authentication **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 **Configuration** - Application Performance - Orchestration - Database Monitoring - Anomaly Detection - Network Security **Model Development** - Language Support - Drag and Drop - Pre-Built Algorithms - Model Training - Database Support - Multi-Language **Database** - Real-Time Data Collection - Data Distribution - Data Lake **Data Transformation** - Real-Time Analytics - Data Querying - Reporting/Analytics - Predictive Analytics - Visual Analytics **Compliance** - Sensitive Data Compliance - Training and Guidelines - Policy Enforcement - Compliance Monitoring - Real-Time Monitoring **Functionality** - Extraction - Transformation - Loading - Automation - Scalability - Non-Relational Transformations - Data Extraction **Management** - Cataloging - Monitoring - Governing - Model Registry **Model Development** - Feature Engineering **Data Modeling and Blending** - Data Querying - Data Filtering - Data Blending - Data Capture and Transfer **Integration** - AI/ ML Integration - BI Tool Integration - Data lake Integration **Security** - Access Control - Roles Management - Compliance Management - Deletion Management - Access Controls/Permissions - User Management - Process Management - Audit Management - Metadata Management **Operations** - Metrics - Infrastructure management - Collaboration **Visualization** - Graphs and Charts - Score Cards - Dashboards - Formats - Mobile Dashboards - Public Dashboards - Private Dashboards **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 - Multimedia Support - Multi-Modal Input Support **Governance & Observability - AI Gateways** - Data Privacy - Cost Tracking - Centralized API Key Security **Additional Functionality** - Data Mapping - Monitoring - Charting - Integration Management - Reporting/Analytics - Ad hoc Analysis - Access Controls/Permissions - API - Match & Merge - Real-Time Monitoring - Metadata Management - Pipeline Management - Job Scheduling - Dashboard Creation - Data Storage Management - Multiple Data Sources - Data Import/Export - Generative AI - Data Quality Control - Data Connectors - Customizable Reports - Single Sign On - Version Control - Visual Analytics - Accounting Integration - Real-Time Data - eCommerce Management - AI Copilot - CRM - Data Visualization - SSL Security - Search/Filter - Real-Time Analytics - Data Capture and Transfer - Collaboration Tools - Performance Management - Data Synchronization - Drag & Drop - Data Replication - Activity Dashboard - Database Support - Workflow Management - Alerts/Notifications - Predictive Analytics - Data Migration - Third-Party Integrations - Reporting & Statistics - Data Analysis Tools **Database Administration** - Provisioning - Governance - Auditing **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 **Maintainence** - Data Quality Management - Policy Management - Data Storage Management **Management** - Cataloging - Monitoring - Governing **Data Updates** - Historical Snapshots - Real-Time Updating **Monitoring and Management** - Data Observability - Testing capabilities **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 **Availability** - Scalability - Backup - Archiving - Indexing **Deployment** - Managed Service - Application - Scalability **Platform** - Machine Scaling - Data Preparation - Spark Integration **Connectivity** - Hadoop Integration - Spark Integration - Multi-Source Analysis - Data Lake - Real-Time Data - Data Capture and Transfer - Trend Analysis - What-if Analysis - Statistical Analysis - Data Blending - Ad hoc Analysis - Third-Party Integrations **Security** - Data Masking - Authentication And Single Sign-On - Data Anonymization **Performance ** - Scalability **Collaboration** - Sharing - Co-Editing - Devices **Cloud Deployment** - Hybrid cloud support - Cloud migration capabilities **Generative AI** - AI Text Generation - AI Text Summarization **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - Third-Party Integrations **Agentic AI - Analytics Platforms** - Autonomous Task Execution - Multi-step Planning - Cross-system Integration - Adaptive Learning - Natural Language Interaction - Proactive Assistance - Decision Making - Third-Party Integrations **Additional Functionality** - Customizable Reports - Collaboration Tools - Data Extraction - Semantic Search - Data Storage Management - Ad hoc Reporting - Reporting/Analytics - Predictive Analytics - Activity Dashboard - Access Controls/Permissions - Visual Analytics - Data Mapping - Data Synchronization - Statistical Analysis - Categorization/Grouping - Trend Analysis - Data Profiling - Linked Data Management - Data Visualization - API - Multiple Data Sources - Sentiment Analysis - Search/Filter - Data Import/Export - Data Capture and Transfer - AI Copilot - Monitoring - Data Connectors - Ad hoc Analysis - Text Mining - Reporting & Statistics - Predictive Modeling - Real-Time Analytics - Configurable Workflow - Tagging - Endpoint Management - No-Code - Data Preparation - Auditing - Big Data Analytics - ML Algorithm Library - Data Management - Activity Tracking - Data Security - Workflow Management **Additional Functionality** - Version Control - Scalability - Personalization - Data Extraction - Webhooks - API - Natural Language Processing - Fallback Handling - Drag & Drop - Multiple LLM Models - Built-in AI Assistant - Automated Testing - Data Governance - Collaboration Tools - Pre-built Templates - Agent Design Tools - Deep Learning - Model Training - Analytics - Single Sign On - Debugging - Deployment Management - Proactive Error Detection **Self Service ** - Calculated Fields - Data Column Filtering - Data Discovery - Search - Collaboration / Workflow - Automodeling - Natural Language Search - Visual Discovery - Data Blending - Data Blending **Processing** - Cloud Processing - Workload Processing **Operations** - Data Visualization - Data Workflow - Governed Discovery - Embedded Analytics - Notebooks - Data Discovery **Data Management** - Data Replication - Advanced Data Analytics **Security** - Data Governance - Data Security **Generative AI** - AI Text Generation - AI Text Summarization - AI Text-to-Image - Generative AI **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 - Third-Party Integrations - Active Directory Integration **Deployment & Integration - Analytics Platforms** - No-code Dashboard Builder - Report Scheduling and Automation - Embedded Analytics and White-labeling - Data Source Connectivity - Multiple Data Sources - Query Builder **Additional Functionality** - AI Copilot - Reporting & Statistics - Data Migration - Archiving & Retention - Data Capture and Transfer - Audit Trail - Data Synchronization - Single Sign On - Customizable Reports - Data Profiling - Authentication - Role-Based Permissions - Tagging - Secure Data Storage - Data Security - HIPAA Compliant - SSL Security - Risk Assessment - Data Mapping - Search/Filter - Self Service Portal - Multiple Data Sources - Document Storage - API - Activity Tracking - Automatic Backup - Visual Analytics **Additional Functionality** - Data Warehousing - Drag & Drop - Activity Dashboard - Data Connectors - Ad hoc Reporting - Customizable Reports - Alerts/Escalation - Templates - Forecasting - Data Migration - Data Transformation - Access Controls/Permissions - API - Data Cleansing - Data Synchronization - AI Copilot - Dashboard Creation - Data Extraction - Data Security - SSL Security - Collaboration Tools - No-Code - High Volume Processing - Database Support - Search/Filter - Generative AI **Advanced Analytics** - Predictive Analytics - Data Visualization - Big Data Services - Real-Time Analytics - Reporting/Analytics - Real-Time Analytics - Reporting/Analytics - Visual Analytics **Generative AI** - AI Text Generation - AI Text Summarization - Generative AI **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 - Third-Party Integrations **Performance & Scalability - Analytics Platforms** - Large data handling and Query Speed - Concurrent User Support - Database Support **Additional Functionality** - Parallel Processing - Ad hoc Analysis - Multiple Data Sources - API - In-Database Processing - Monitoring - Real-Time Reporting - Data Synchronization - Real-Time Monitoring - Data Connectors - Performance Metrics - Ad hoc Reporting - Alerts/Notifications - Access Controls/Permissions - Drag & Drop - Data Visualization - Data Import/Export - Match & Merge - Data Transformation - Secure Data Storage - Data Extraction - AI Copilot - Customizable Reports - Activity Dashboard - Data Migration - In-Memory Processing - Data Mapping **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 - Ad hoc Query - Visual Analytics **Building Reports** - Data Transformation - Data Modeling - WYSIWYG Report Design - Integration APIs - Real-Time Data - Real-Time Data - Third-Party Integrations - Third-Party Integrations **Platform** - Mobile User Support - Customization - User, Role, and Access Management - Internationalization - Sandbox / Test Environments - Performance and Reliability - Breadth of Partner Applications - Mobile Access - Metadata Management **Data Updates** - Historical Snapshots - Real-Time Updating - Scheduled/Automated Reports - Customizable Reports - Predictive Analytics - Data Management - Real-Time Data **Additional Functionality** - Drag & Drop - Secure Data Storage - Data Import/Export - Customizable Branding - Dashboard Creation - Access Controls/Permissions - Real-Time Reporting - Metadata Management - Forecasting - Data Storage Management - Ad hoc Analysis - Ad hoc Reporting - Data Migration - AI Copilot - Self Service Data Preparation - Search/Filter - Data Mapping - Monitoring - Sentiment Analysis - User Management - Sales Trend Analysis - Charting - Reporting & Statistics - Performance Metrics - Alerts/Notifications - Data Extraction - Widgets - Storytelling - Trend Analysis - Collaboration Tools - Financing Management - Self-service Analytics - KPI Monitoring - Dashboard - API - Data Integration - Scorecards - Data Management - Task Management - Progress Tracking - Data Connectors - Data Visualization - Profitability Analysis - Project Tracking - Strategic Planning - Goal Setting/Tracking - Publishing/Sharing - Predictive Analytics - Real-Time Monitoring - Templates - Mobile Access - Customizable Templates - Workflow Management - Financial Reporting - Audit Management - OLAP - Single Sign On - Data Synchronization **Additional Functionality** - Customizable Branding - Natural Language Processing - Data Extraction - AI Copilot - Ad hoc Reporting - Real-Time Reporting - Publishing/Sharing - Collaboration Tools - Strategic Planning - Self Service Data Preparation - Trend Analysis - Text Analysis - Real-Time Monitoring - Data Synchronization - Widgets - Benchmarking - Performance Metrics - Data Mapping - Generative AI - Trend/Problem Indicators - Customizable Templates - Access Controls/Permissions - Data Import/Export - Profitability Analysis - OLAP - Alerts/Notifications - Search/Filter - Drag & Drop - Data Connectors - Multiple Data Sources - Key Performance Indicators - Dashboard Creation - Role-Based Permissions - Data Mining - Forecasting - Ad hoc Query **Additional Functionality** - KPI Monitoring - Secure Data Storage - Multiple Data Sources - Data Visualization - Single Sign On - Natural Language Search - Search/Filter - Alerts/Notifications - AI Copilot - Real-Time Notifications - Collaboration Tools - Widgets - Performance Metrics - Ad hoc Reporting - Activity Tracking - Reporting/Analytics - Customizable Branding - Data Aggregation - Dashboard Creation - Data Connectors - Customizable Templates - Data Synchronization - API - Forecasting - Interactive Elements - Single Page View - Data Import/Export - Drag & Drop - Access Controls/Permissions - Trend Analysis - Workflow Management - Third-Party Integrations - Functions/Calculations - Historical Reporting - Data Capture and Transfer - Visual Discovery - Real-Time Updates - Real-Time Reporting - Data Mapping - Relational Display - Visual Analytics - OLAP - Ad hoc Query - Reporting & Statistics ## 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 (713 reviews) - [Teradata Autonomous Knowledge Platform](https://www.g2.com/products/teradata-autonomous-knowledge-platform/reviews) - 4.3/5.0 (356 reviews)