Databricks Data Intelligence Platform
Databricks, Inc.External reviews
640 reviews
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All in one Tool
What do you like best about the product?
I like how easy it is to seamlessly load data and also the ai facility to attach data.
What do you dislike about the product?
Least helpful currently i guess is lineage for me. It dosent give good lineage diagram
What problems is the product solving and how is that benefiting you?
It is solving to create one data platform
Shaping the future with AI
What do you like best about the product?
Integration across their other products within Databricks
What do you dislike about the product?
I think there is nothing at this moment that I don’t like it
What problems is the product solving and how is that benefiting you?
It is solving the problems of data governance
Excellent
What do you like best about the product?
It offers a lot for notebook users.Users are easily able to write spark jobs.
What do you dislike about the product?
For now we don’t think of anything else.
What problems is the product solving and how is that benefiting you?
It’s a one stop platform for all spark related use cases.
Databricks with Unity Catalog
What do you like best about the product?
Centralized management of data assets across environments through the Unity Catalog
What do you dislike about the product?
Single megastore per region and having very few metastore admins across the enterprise can make enterprise adoption challenging without having access to Databricks premium support
What problems is the product solving and how is that benefiting you?
Centralizing data assets under a single umbrella. Ingest data once and share using delta share instead of doing data copies which enables data to be consistent across the enterprise. Good lineage
Drive up your data processing efficiency
What do you like best about the product?
A robust platform that helps to improve data processing capabilities
What do you dislike about the product?
Though in my early stages of adoption, the platform is working well for us so far.
What problems is the product solving and how is that benefiting you?
Improves processing efficiency
Improves cost to processing ratio
Improves cost to processing ratio
Databricks is the Swiss Army knife of data
What do you like best about the product?
Makes data processing streamlined and easy to go end 2 end
What do you dislike about the product?
It is still very spark centric, although new features are improving on this
What problems is the product solving and how is that benefiting you?
Data processing
Databricks, the furture of BI and AI
What do you like best about the product?
Best reliable high speed and high availability cloud Data Intelligence platform that you can count on.
What do you dislike about the product?
Learning curve is a bit high. A bit pricey too.
What problems is the product solving and how is that benefiting you?
Close to realtime analytics with millions of rows per hour.
Databricks has unlocked ML and AI for our data
What do you like best about the product?
ease of model hosting / serving and our availing to always have the latest and greatest with foundation models
What do you dislike about the product?
It is not very intuitive as a platform — lots of different permissions and compute is confusing
What problems is the product solving and how is that benefiting you?
Previously, any ML or AI was siloed off. Now we benefit from it all being in one place which makes orchestration much easier
Speed of execution and cutting-edge innovation
What do you like best about the product?
First, I like its integration with Microsoft Azure. Second, I like the computing power of the Spark clusters. Third, I like the ML library implementations.
What do you dislike about the product?
Occasionally, IT jobs fail because clusters could not start or crashed in the middle of a job execution. This is not necessarily Databricks' fault.
What problems is the product solving and how is that benefiting you?
Databricks provides a software platform that powers powerful, distributed execution of complex code and ML algorithm implementations over big data. This produces forecasts, recommendations, and analysis that power and scale up business processes and support customers, users, and executive decision making.
Empowering Data Teams with Unified Intelligence and Performance
What do you like best about the product?
Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably.
What do you dislike about the product?
While powerful, the platform has a learning curve—especially for teams unfamiliar with Spark or distributed computing. Some features (like Unity Catalog or serverless compute) can be region-specific or limited by cloud provider compatibility. Additionally, job debugging and cluster cost management can be challenging without careful monitoring and tagging, particularly in enterprise-scale projects.
What problems is the product solving and how is that benefiting you?
Databricks solves the critical problem of data fragmentation by unifying data engineering, data science, analytics, and governance in one platform. Previously, we had to stitch together multiple tools—ETL frameworks, notebooks, ML platforms, and data warehouses. With Databricks, everything from ingestion to model deployment happens in one place, drastically reducing complexity and context-switching.
Another key problem it addresses is scalability and performance for big data workloads. The platform’s native support for Apache Spark and Delta Lake enables reliable, fast processing of massive datasets. This helps us run ML pipelines and analytics at scale without worrying about infrastructure bottlenecks.
Another key problem it addresses is scalability and performance for big data workloads. The platform’s native support for Apache Spark and Delta Lake enables reliable, fast processing of massive datasets. This helps us run ML pipelines and analytics at scale without worrying about infrastructure bottlenecks.
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