Delta Lake

Delta Lake is an open source storage layer that brings reliability to data lakes. Delta Lake provides ACID transactions, scalable metadata handling, and unifies streaming and batch data processing. Delta Lake runs on top of your existing data lake and is fully compatible with Apache Spark APIs.

Delta Lake on Databricks allows you to configure Delta Lake based on your workload patterns and provides optimized layouts and indexes for fast interactive queries.

This is the documentation for Delta Lake on Databricks.

• Introduction to Delta Lake
  • Quickstart
  • Resources
• Introductory Notebooks
  • Delta Lake Quickstart Python notebook
  • Delta Lake Quickstart Scala notebook
  • Delta Lake Quickstart SQL notebook
• Table Batch Reads and Writes
  • Create a table
  • Read a table
  • Write to a table
  • Schema validation
  • Update table schema
  • Replace table schema
  • Views on tables
  • Table properties
  • Table metadata
• Table Streaming Reads and Writes
  • Delta table as a stream source
  • Delta table as a sink
• Table Deletes, Updates, and Merges
  • Delete from a table
  • Update a table
  • Upsert into a table using Merge
  • Merge examples
• Table Utility Commands
  • Vacuum
  • History
  • Convert to Delta
• Delta Lake API Reference
• Concurrency Control
  • Optimistic concurrency control
• Migrate Workloads to Delta Lake on Databricks
  • Example
• Optimizations
  • Optimizing Performance with File Management
  • Auto Optimize
  • Optimizing Performance with Caching
  • Isolation Levels
  • Replicating MySQL Tables to Delta Lake Tables
  • Optimize Join Performance
  • Optimized Data Transformation
  • Table Versioning
• Presto and Athena Compatibility Support for Delta Lake
  • Query a Delta table from Presto or Athena
  • Workflow with Databricks and Presto or Athena using the same Hive metastore
  • Limitations
• Frequently Asked Questions (FAQ)
  • What is Delta Lake?
  • How is Delta Lake related to Apache Spark?
  • What format does Delta Lake use to store data?
  • How can I read and write data with Delta Lake?
  • Where does Delta Lake store the data?
  • Can I stream data directly into Delta tables?
  • Can I stream data from Delta tables?
  • Does Delta Lake support writes or reads using the Spark Streaming DStream API?
  • When I use Delta Lake, will I be able to port my code to other Spark platforms easily?
  • How do Delta tables compare to Hive SerDe tables?
  • Does Delta Lake support multi-table transactions?
  • What DDL and DML features does Delta Lake not support?
  • How can I change the type of a column?
  • When should I use partitioning with Delta tables?
  • When should I use Z-ORDER BY with Delta tables?
  • What does it mean that Delta Lake supports multi-cluster writes?
  • What are the limitations of multi-cluster writes?
  • Why is Delta Lake data I deleted still stored in S3?
  • Why does a table show old data after I delete Delta Lake files with rm -rf and create a new table in the same location?
  • Can I access Delta tables outside of Databricks Runtime?
• Additional Resources
  • Blog posts
  • Talks
  • Delta Lake transaction log specification