Delta Lake is an open source project that enables building a Lakehouse architecture on top of data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing on top of existing data lakes.
Specifically, Delta Lake offers:
- ACID transactions on Spark: Serializable isolation levels ensure that readers never see inconsistent data.
- Scalable metadata handling: Leverages Spark distributed processing power to handle all the metadata for petabyte-scale tables with billions of files at ease.
- Streaming and batch unification: A table in Delta Lake is a batch table as well as a streaming source and sink. Streaming data ingest, batch historic backfill, interactive queries all just work out of the box.
- Schema enforcement: Automatically handles schema variations to prevent insertion of bad records during ingestion.
- Time travel: Data versioning enables rollbacks, full historical audit trails, and reproducible machine learning experiments.
- Upserts and deletes: Supports merge, update and delete operations to enable complex use cases like change-data-capture, slowly-changing-dimension (SCD) operations, streaming upserts, and so on.
Delta Engine optimizations make Delta Lake operations highly performant, supporting a variety of workloads ranging from large-scale ETL processing to ad-hoc, interactive queries. For information on Delta Engine, see Delta Engine.
The Delta Lake quickstart provides an overview of the basics of working with Delta Lake. The quickstart shows how to build pipeline that reads JSON data into a Delta table, modify the table, read the table, display table history, and optimize the table.
For Databricks notebooks that demonstrate these features, see Introductory notebooks.
To try out Delta Lake, see Sign up for Databricks.