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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.
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.
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.
Saloni N.
Databricks Makes Real-World Data Pipelines Easy with Notebooks, Spark, and Delta Lake
Reviewed on Oct 19, 2026
Review provided by G2
What do you like best about the product?
I like Databricks because it gives me a practical environment to work with data processing and transformation using Python, PySpark, and SQL. I can explore datasets, clean inconsistent records, remove duplicates, and prepare structured data for reporting. I also find the notebook environment helpful because I can test my code step by step and understand the result immediately. I like notebooks, PySpark DataFrames, Spark SQL, Delta Lakes, and job scheduling. Notebooks make development easier, while Delta Lake helps to manage reliable tables and supports operations such as updates and incremental processing. These features are useful for learning real-world data pipeline development. The workspace is well-suited for writing and testing code in notebooks; running individual cells and viewing output makes debugging easier. However, it works well with cloud storage, data orchestration tools, and other data platforms. Connecting it with Azure Data Lake Storage and Azure Data Factory is useful for building end-to-end data pipelines.
What do you dislike about the product?
The platform has many features, so it can take time to understand the workspace compute settings, permissions, and job configuration. Clustering starting times can sometimes interrupt development, and I need to be careful with compute usage to avoid unnecessary costs.
What problems is the product solving and how is that benefiting you?
Databricks helps me process data that would be difficult to handle efficiently with basic scripts alone. I can combine data from different sources, apply transformation rules, perform data quality checks, and organize datasets into bronze, silver, and gold layers. This gives me a better understanding of how raw data becomes reliable, analytics-ready information. It performs well when processing large datasets with Spark. I can use DataFrame operations and Spark SQL to transform data and analyze results. Performance depends on cluster configuration, data size, partitioning, and the way transformations are written, so I am still developing my optimization skills. The AI-assisted development features can help explain code, suggest improvements, and speed up SQL or PySpark development where available.
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.
James W.
Databricks’ End-to-End Unified Data Lake Accelerated Our Delivery
Reviewed on Oct 14, 2026
Review provided by G2
What do you like best about the product?
The best thing I like about Databricks is its overall end-to-end experience which makes it different from the other solutions. As a project manager, we had used Databricks in our organization for almost 3 years. In those 3 years, we have used it for its unified data lake for data warehouse. Also, it has helped us to reduce the time to market for our projects by almost 45%. With its unified environment, it has helped our teams to reduce friction and to move at a faster pace.
What do you dislike about the product?
There are no particular things that I dislike, but they may improve on auto-scaling compute since it can get small companies in trouble as it may create a budget overhead. Also, it is a code-heavy platform, which also means that non-tech users can't use it much.
What problems is the product solving and how is that benefiting you?
It has helped us in environment unification which helped our teams collaborate better and achieve goals at a 50% faster pace. Secondly, due to its seamless UI and integrations within our systems, we have eliminated working in silos and minimized technical blockers.