Azure Databricks is a fully managed Apache Spark platform built by Databricks and deeply integrated with Azure, combining managed Spark clusters, collaborative notebooks, Delta Lake storage, Unity Catalog for data governance, MLflow for ML experiment tracking, and Databricks Workflows for pipeline orchestration. It is the most feature-rich data engineering platform on Azure for teams working primarily with Python and Spark — offering a developer experience significantly better than raw Synapse Spark pools or EMR, with faster cluster startup, auto-scaling, interactive debugging, and a rich SQL analytics interface.
The Databricks lakehouse architecture positions Delta Lake as the storage layer, Unity Catalog as the governance layer, and Databricks Runtime as the execution layer. Unity Catalog provides a single, centralised governance system for all data assets — tables, files, machine learning models, and dashboards — across all Databricks workspaces in the organisation. Fine-grained access control at the column and row level, automated data lineage tracking, and audit logging make Unity Catalog the recommended governance approach for organisations with compliance requirements around data access and sensitivity classification.