Databricks
Data · Enterprise
Overview
A platform for storing large amounts of data and building analytics and AI on top of it in one place. It was founded in 2013 by the team behind Apache Spark, the open source engine for large scale data processing. Its approach is called the lakehouse, which mixes the cheap storage of a data lake with the structure and reliability of a data warehouse. The core pieces are Spark for processing, Delta Lake for reliable storage, notebooks for writing code, Unity Catalog for governance, and MLflow for managing machine learning work. It runs on Amazon, Microsoft, and Google cloud infrastructure.
What people use it for
Data teams use Databricks to clean and transform large datasets in the pipelines that feed dashboards and products. They run the data engineering jobs that move and reshape data on a schedule. They train, track, and deploy machine learning models with MLflow. They run SQL analytics on lakehouse data through Databricks SQL and the Photon engine. They govern access to tables, files, and models across an organization with Unity Catalog. They share live data with outside partners through Delta Sharing without copying it. They also build and serve AI features, including work with large language models, on the same platform that holds the data.
Key capabilities
Databricks is built on the open source projects Apache Spark, Delta Lake, and MLflow. Delta Lake adds transactions, schema enforcement, and time travel to files in cloud storage. Unity Catalog is a single place to set data access policies across all workspaces and covers tables, files, features, and models. The Photon engine is a C++ query engine that speeds up SQL and DataFrame work. Notebooks support Python, SQL, Scala, and R and allow shared editing. MLflow manages experiments, model versions, and deployment. Delta Sharing sends live data to other platforms without replication. It runs on AWS, Azure, and Google Cloud, with Azure Databricks sold as a first party Microsoft service. Pricing is usage based on units called DBUs, on top of the underlying cloud compute cost, and varies by workload type.
Limitations
The learning curve is steep, especially for teams not already comfortable with Spark, notebooks, and cloud data platforms. It assumes people who write code, so it is not for casual analysts. Cost is hard to predict and can climb fast when workloads scale, and cost reporting is not always transparent. Debugging performance problems in large pipelines is difficult. Advanced tuning needs experienced staff, which slows adoption at smaller or less mature data teams. Setup and administration are heavier than a plain warehouse. Much of the value only shows up on genuine data engineering and machine learning work.
Insight
Databricks is strongest when one team both processes large datasets and builds models, because it keeps that work on a single platform with shared governance. If the need is really just dashboards and SQL, a plain warehouse is simpler and cheaper. The learning curve is real. Teams without Spark experience spend weeks getting comfortable, and the people who get value from it are engineers, not spreadsheet analysts. Cost follows the same pattern as other usage based data tools: predictable only if someone watches it, expensive if no one does. The platform is capable and the open source foundation lowers lock in worry somewhat, but it rewards teams that are already doing serious data engineering. For a small team that mostly wants reports, it is more machine than the job needs.
Pricing
Enterprise
Website
Last checked 2026-08-30