Unified lakehouse platform for data engineering, analytics and AI
Databricks is a data and AI platform built by the team behind Apache Spark. It packages a managed lakehouse - Delta Lake tables, SQL warehouses, notebooks and job orchestration - together with a full model lifecycle stack for training, serving and monitoring machine learning. Because the storage layer stays open, the same tables are readable from Spark, SQL and Python without copying data between systems. Its differentiator is governance: Unity Catalog tracks permissions and lineage across tables, notebooks, features and deployed model endpoints in one place, which is hard to reproduce by stitching separate engineering, BI and ML tools together.
Last updated: 2026-09-20. This site only provides an index; for exact features, pricing, and licensing, see the official website.
data engineering, lakehouse analytics, ml pipelines and large-scale etl
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Yes. The free edition gives a limited workspace with notebooks, clusters and sample data, which is enough to learn the interface and test Spark code. It is a practice environment rather than something to run production workloads on, and it comes with usage caps.
No. Tables are stored in Delta Lake and Parquet on object storage you control, and the SQL engine and Spark APIs are open source. You can read the same files with other engines, though features such as Unity Catalog permissions only apply inside the platform.
It covers model training and tracking through MLflow, feature engineering, model serving endpoints and hosted foundation model APIs for text tasks. Separate tools still handle dashboarding or application front ends, which read from the same governed tables.
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