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Databricks Data Intelligence Platform
The Databricks Data Intelligence Platform unlocks the power of data and AI for your entire organization. Enjoy up to $400 in usage credits during your 14-day free trial. Cancel anytime. After your trial ends, you will automatically be enrolled into a Databricks pay-as-you-go plan.
Reviews (1410)
Diego Pablo
Data workflows have become faster and now integrate diverse sources for timely analytics
Reviewed on Oct 24, 2026
Review provided by PeerSpot
What is our primary use case?
Databricks was utilized by our team when we needed to undergo a comprehensive change in the data area and structure the architecture. Day to day, I run queries on the database, fetching data and information using SQL, so that in the end we have what the stakeholders asked us for, and then we productize that, pushing it to Looker or a data visualization tool that the company wants or needs.
The whole change of platforms within a company is quite challenging, and when we made the switch to Databricks, the difference was huge.
How has it helped my organization?
Databricks helps the company as a whole integrate data from different sources, automate processes, and make up-to-date information available for analysis.
The reduction of rework is a huge gain, and data refresh now takes one hour or less with Databricks, representing a reduction of almost seventy-five percent or more in time.
What is most valuable?
The best features that Databricks offers are data integration and transformation, running analyses with SQL and Python, processing large volumes of data with Spark, automating pipelines, collaborating in notebooks, and developing AI and Machine Learning solutions with centralized governance and access control.
Pipeline automation in my day-to-day makes things easier by scheduling and running data tasks, like collecting information, transforming datasets, and updating tables, which greatly reduces manual work.
Databricks' Artificial Intelligence features are useful, helping in areas such as assisting with code creation and speeding up queries, and in terms of governance and security, access control and traceability help a lot.
The AI features are useful for gaining productivity, but the reliability of results depends on data quality, so it is very important to review the generated code and validate the results before using them in company decisions.
What needs improvement?
Databricks could be improved with a more intuitive interface, clearer error messages, and practical guidance that also helps a lot when configuring pipelines.
For how long have I used the solution?
I have been using Databricks for three years.
What do I think about the stability of the solution?
Databricks is stable.
What do I think about the scalability of the solution?
Databricks offers very good scalability, allowing you to increase or reduce processing resources according to the volume of data and demand.
How are customer service and support?
I have not had any problems with customer support, as the few times we needed it, it was resolved in a timely manner.
I would rate customer support a nine.
Which solution did I use previously and why did I switch?
Before, I used Snowflake, and we switched mainly due to costs.
What was our ROI?
While I do not have much information, I can see that the data team has improved significantly with Databricks.
What's my experience with pricing, setup cost, and licensing?
I have had a very good experience with pricing, as there was a drop in costs compared to the previous tool.
Which other solutions did I evaluate?
The idea was only Databricks, already due to the partnership.
What other advice do I have?
I would rate Databricks a nine because it makes integration easier and brings agility and productivity; to reach ten, I see room to make the experience more intuitive.
I would advise others to run a trial first and use the tool because it is reliable, good, scalable, works very well, and has many features built in.
Meriam B.
Flexible Interface and Fast Segmentation Testing with Databricks
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
I like the interface—it’s flexible—and the speed at which you can test a segmentation hypothesis is excellent. Seeing results in a matter of hours is fantastic. I also love being able to sit down and collaborate directly with the data engineers; it’s great. Databricks does a magnificent job.
What do you dislike about the product?
Storage costs are rising. Also, I want to be able to run complex analyses without having to write PySpark code. That’s it.
What problems is the product solving and how is that benefiting you?
Databricks is excellent for real-world use cases, such as evaluating brand perception and identifying gaps. It has been a lifesaver during quarterly business reviews. When the Chief Marketing Office asks why adoption of a specific feature has declined, I can provide data-driven answers instead of resorting to improvised excuses.
Brian M.
Easy to Use with Agents
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
It’s easy to use, and we have good access to support from our Databricks support and account team. The documentation is also clear and straightforward.
What do you dislike about the product?
Onboarding can have more friction than I’d prefer.
What problems is the product solving and how is that benefiting you?
It’s helped me solve data accessibility and infrastructure availability for data analytics and data science pipelines. I honestly wouldn’t have known where to start with building an ML model without Databricks.
Jean M.
Powerful Data Sharing and Scaling, with Cost and UI Trade-Offs
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
I like that Databricks lets me apply complex transformations and rely on a single source of truth. I value being able to share data with my team so they understand my business logic. Automatic scaling is valuable at the end of each quarter for in-depth customer churn and upsell analysis, and it helps high-volume processes run quickly without the system crashing. Setup was very easy in our environment.
What do you dislike about the product?
I think Databricks’ interface could be improved because it seems too focused on machine learning and generic analytics. I also have to keep a close eye on costs, especially toward the end of the billing cycle, because usage can increase significantly.
What problems is the product solving and how is that benefiting you?
I use Databricks to consolidate customer data across systems, apply complex transformations, and rely on a single source of truth. I can share data so my team understands my business logic, and automatic scaling helps with quarter-end churn and upsell analyses and keeps high-volume processes running quickly without crashing.
Shrushti T.
Powerful Databricks ETL at Scale, with a Learning Curve and Cost Tuning Needed
Reviewed on Oct 22, 2026
Review provided by G2
What do you like best about the product?
I like Databricks because it provides a flexible environment for building and maintaining data transformation pipelines at scale. I can use PySpark and Spark SQL to handle complex transformations, process large datasets, and implement incremental data loads. Delta Lake is particularly useful for managing reliable tables and handling updates without relying on multiple disconnected process tools. It helps ETL development by bringing data processing transformation and pipeline management into a unified workspace. I can consolidate data from multiple sources, apply business rules, perform data quality checks, and prepare curated datasets for downstream analytics. Its features like PySpark, Spark SQL, Delta Lake workflow scheduling, and Medallion architecture make Delta tables useful for reliable data updates. It provides a convenient way to develop, test, and ETL logic. Notebook supports collaboration, and job monitoring helps track pipeline executions. Its integration with cloud storage, orchestration tools, and data warehouse, combining Azure Data Factory, Azure Data Lake Storage, and Databricks, is useful for creating end-to-end ETL pipelines.
What do you dislike about the product?
It has a learning curve, especially when working with advanced cluster configuration, access control, and performance tuning. Troubleshooting inefficient Spark jobs can take time when dealing with skewed data or memory issues. Compute costs also require attention when workspaces are not configured efficiently.
What problems is the product solving and how is that benefiting you?
I can consolidate data from multiple sources, apply business rules, perform data quality checks, and prepare curated datasets for downstream analytics. This reduces the complexity of maintaining separate processing environments and makes pipelines easier to manage. It provides a convenient way to develop, test, and implement ETL logic. Notebook supports collaboration, and job monitoring helps track pipeline executions. Managing catalogs, permissions, compute resources, and production configuration requires familiarity with the platform.
Matthieu D.
One Unified Workspace for Notebooks, SQL, and Delta Lake
Reviewed on Oct 21, 2026
Review provided by G2
What do you like best about the product?
I like having notebooks, SQL, and Delta Lake combined in a single environment. It’s excellent because I can move from exploring data to building a production process without needing to switch tools. I also appreciate the integration with BI and ML tools, which makes it easier to connect analysis and modeling in the same workflow.
What do you dislike about the product?
What I don’t like is that the initial setup—permissions, environments, and conventions—ends up being very complex, more than I expected, especially in companies with many teams and sensitive data.
What problems is the product solving and how is that benefiting you?
Overall, Databricks organizes data much better. It really excels when you’re working with multiple sources, large volumes, and diverse teams that all need to consume the same information. It’s excellent—I hope it stays that way.
Gota N.
Databricks Boosted Delivery Speed and Team Collaboration
Reviewed on Oct 20, 2026
Review provided by G2
What do you like best about the product?
The best thing that I admire about Databricks is the strong data expertise that it has provided us, also they have helped us to bridge the gap between our engineering and. ML along with BI tracks. It's has helped us to speed up the delivery timelines by 39.89% Also With the ETL pipelines has helped us to deploy without upfront infra provisioning. We have seen a change in collaboration since teams are able to collaborate better by 58% since it has vc integrations and multi language support such as (Pyspark, SQL & Scala).
What do you dislike about the product?
There are things which I feel that could be improved in Databricks, first thing is the consumption based model can escalate quickly and it is something not suitable for small and mid businesses. Second is learning curve it is hard to learn and it has complex architecture involved in it. Third, the integrations and dashboards can be better and more intuitive.
What problems is the product solving and how is that benefiting you?
Databricks has helped us to manage the pipelines across independent data warehouses & helped us to separate ML tools and varied our storage buckets. It has also helped us to simplify our sprint road maps and shortened our sprint to production cycles by 67%. And Delivery has been boosted by 56%.
Camille C.
Fast, Governed Analytics—With Notebook Git Friction
Reviewed on Oct 20, 2026
Review provided by G2
What do you like best about the product?
I like that Databricks brings our finance exports, timesheets, pipeline, and Revit quantity schedules into one governed layer, so I can connect fee performance with resourcing without the weekly reconciliation chore, and our post-occupancy energy models use those same tables. Autoscaling on Delta Lake work is what sold me. With Photon, aggregations across years of project and utilization history run fast enough that I’ll actually iterate on a model instead of kicking it off and going to make coffee. Unity Catalog is the other piece I’d fight to keep: partners see exactly the tables they should and nothing else, and the lineage view helped settle a disagreement about a margin figure that had been going in circles for a fortnight.
What do you dislike about the product?
Git integration for notebooks leaves a good deal to be desired. When two of us touch the same notebook, merge conflicts take real effort to untangle, so I have taken to claiming files outright. I’d like cell-by-cell conflicts with a proper side-by-side diff and the option to resolve them in the workspace. Large result sets in workspace cells can also lock up the tab, so I sample before displaying anything substantial. Setup was steep at the start, mainly getting Unity Catalog structured properly.
What problems is the product solving and how is that benefiting you?
I use Databricks to pull finance exports, timesheets, pipeline, and Revit quantity schedules into one governed layer, tying fee performance to resourcing without the weekly reconciliation chore; our post-occupancy energy models run off the same tables.
Sunny C.
High-Performance PySpark + NLP Workflows in One Place
Reviewed on Oct 15, 2026
Review provided by G2
What do you like best about the product?
I like being able to integrate PySpark and natural language processing (NLP) models into a single workflow. It lets me query historical records with very high performance using SQL, while also running Python scripts to group results by keywords.
What do you dislike about the product?
Computing costs can be high, and cluster management expenses can escalate quickly if you don’t keep tight control over autoscaling and idle time.
What problems is the product solving and how is that benefiting you?
I use it extensively for analytical processing and optimization, specifically for response methods and websites that have a large number of URLs tied to a complex e-commerce platform. This includes processing crawl data, JavaScript rendering, and AI bot logs. By combining these elements with the capabilities of traditional SEO tools and Databricks, we’re able to handle massive volumes of data and analyze indexing patterns in real time.
Laurel B.
Streamlines Compliance Data, but Needs More No-Code Dashboards
Reviewed on Oct 14, 2026
Review provided by G2
What do you like best about the product?
I like that Databricks lets me consolidate all our information in a lakehouse architecture in a clean and efficient way. I can create models and automated alerts for discrepancies in taxes, duties paid, or expired permits, which enables us to maintain a proactive approach to compliance. It replaces massive Excel files and manual processes with automated data models that cross-reference customs declarations with internal accounting records. The initial setup was easy.
What do you dislike about the product?
I would like Databricks to have more no-code templates or preconfigured native dashboards for business and regulatory compliance users.
What problems is the product solving and how is that benefiting you?
I use Databricks to replace massive Excel files and manual processes with automated data models that cross-reference customs declarations against internal accounting records.