Get Started with Delta Lake


Delta Lake is an open-source storage layer that adds relational database semantics to Spark-based data lake processing. Delta Lake is supported in Azure Synapse Analytics Spark pools for PySpark, Scala, and .NET code.

The benefits of using Delta Lake in Azure Databricks include:

  • Relational tables that support querying and data modification. With Delta Lake, you can store data in tables that support CRUD (create, read, update, and delete) operations. In other words, you can select, insert, update, and delete rows of data in the same way you would in a relational database system.
  • Support for ACID transactions. Relational databases are designed to support transactional data modifications that provide atomicity (transactions complete as a single unit of work), consistency (transactions leave the database in a consistent state), isolation (in-process transactions can't interfere with one another), and durability (when a transaction completes, the changes it made are persisted). Delta Lake brings this same transactional support to Spark by implementing a transaction log and enforcing serializable isolation for concurrent operations.
  • Data versioning and time travel. Because all transactions are logged in the transaction log, you can track multiple versions of each table row, and even use the time travel feature to retrieve a previous version of a row in a query.
  • Support for batch and streaming data. While most relational databases include tables that store static data, Spark includes native support for streaming data through the Spark Structured Streaming API. Delta Lake tables can be used as both sinks (destinations) and sources for streaming data.
  • Standard formats and interoperability. The underlying data for Delta Lake tables is stored in Parquet format, which is commonly used in data lake ingestion pipelines.


For more information about Delta Lake in Azure Databricks, see the Delta Lake guide in the Azure Databricks documentation.