Welcome to the Delta Lake documentation

Note

Delta Lake 4.0 Preview is released! See the 4.0 Preview documentation here.

This is the documentation site for Delta Lake.

• Introduction
• Quickstart
  • Set up Apache Spark with Delta Lake
  • Create a table
  • Read data
  • Update table data
  • Read older versions of data using time travel
  • Write a stream of data to a table
  • Read a stream of changes from a table
• Table batch reads and writes
  • Create a table
  • Read a table
  • Query an older snapshot of a table (time travel)
  • Write to a table
  • Schema validation
  • Update table schema
  • Replace table schema
  • Views on tables
  • Table properties
  • Syncing table schema and properties to the Hive metastore
  • Table metadata
  • Configure SparkSession
  • Configure storage credentials
• Table streaming reads and writes
  • Delta table as a source
  • Delta table as a sink
  • Idempotent table writes in foreachBatch
• Table deletes, updates, and merges
  • Delete from a table
  • Update a table
  • Upsert into a table using merge
  • Special considerations for schemas that contain arrays of structs
  • Merge examples
• Change data feed
  • Use cases
  • Enable change data feed
  • Read changes in batch queries
  • Read changes in streaming queries
  • What is the schema for the change data feed?
  • Change data feed limitations for tables with column mapping enabled
  • Frequently asked questions (FAQ)
• Table utility commands
  • Remove files no longer referenced by a Delta table
  • Retrieve Delta table history
  • Retrieve Delta table details
  • Generate a manifest file
  • Convert a Parquet table to a Delta table
  • Convert an Iceberg table to a Delta table
  • Convert a Delta table to a Parquet table
  • Restore a Delta table to an earlier state
  • Shallow clone a Delta table
  • Clone Parquet or Iceberg table to Delta
• Constraints
  • NOT NULL constraint
  • CHECK constraint
• How does Delta Lake manage feature compatibility?
  • What Delta Lake features require client upgrades?
  • What is a table protocol specification?
  • Which protocols must be upgraded?
  • What are table features?
  • Do table features change how Delta Lake features are enabled?
  • What is a protocol version?
  • Features by protocol version
  • Upgrading protocol versions
• Delta default column values
  • How to enable Delta Lake default column values
  • How to use default columns in SQL commands
• Delta column mapping
  • How to enable Delta Lake column mapping
  • Rename a column
  • Drop columns
  • Supported characters in column names
  • Known limitations
• Use liquid clustering for Delta tables
  • What is liquid clustering used for?
  • Enable liquid clustering
  • Choose clustering columns
  • Write data to a clustered table
  • How to trigger clustering
  • Read data from a clustered table
  • Change clustering columns
  • See how table is clustered
  • Limitations
• What are deletion vectors?
  • Enable deletion vectors
  • Apply changes to Parquet data files
• Drop Delta table features
  • How can I drop a Delta table feature?
  • What Delta table features can be dropped?
  • How are Delta table features dropped?
• Delta Lake APIs
  • Delta Spark
  • Delta Standalone
  • Delta Flink
  • Delta Kernel
• Delta Connect (aka Spark Connect Support in Delta)
  • Motivation
  • How to start the Spark Server with Delta
  • How to use the Python Spark Connect Client with Delta
  • How to use the Scala Spark Connect Client with Delta
  • Preview Limitations
• Storage configuration
  • Amazon S3
  • Microsoft Azure storage
  • HDFS
  • Google Cloud Storage
  • Oracle Cloud Infrastructure
  • IBM Cloud Object Storage
• Delta Coordinated Commits
• DynamoDB Commit Coordinator
  • Quickstart Guide
  • Removing the Coordinated Commits Feature
  • Compatibility
  • Dependencies
• Delta type widening
  • Supported type changes
  • How to enable Delta Lake type widening
  • Manually applying a type change
  • Type changes with automatic schema evolution
  • Removing the type widening table feature
• Universal Format (UniForm)
  • Requirements
  • Enable Delta Lake UniForm
  • When does UniForm generate metadata?
  • Check Iceberg/Hudi metadata generation status
  • Read UniForm tables as Iceberg tables in Apache Spark
  • Read UniForm tables as Iceberg tables using a metadata JSON path
  • Read UniForm tables as Hudi tables in Apache Spark
  • Delta and Iceberg/Hudi table versions
  • Limitations
• Read Delta Sharing Tables
  • Read a snapshot
  • Query an older snapshot of a shared table (time travel)
  • Read Table Changes (CDF)
  • Streaming
  • Read Advanced Delta Lake Features in Delta Sharing
• Concurrency control
  • Optimistic concurrency control
  • Write conflicts
  • Avoid conflicts using partitioning and disjoint command conditions
  • Conflict exceptions
• Access Delta tables from external data processing engines
  • Presto to Delta Lake integration
  • Trino to Delta Lake integration
  • Athena to Delta Lake integration
  • Other integrations
• Migration guide
  • Migrate workloads to Delta Lake
  • Migrate Delta Lake workloads to newer versions
• Best practices
  • Choose the right partition column
  • Compact files
  • Replace the content or schema of a table
  • Spark caching
• 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 copy my Delta Lake table to another location?
  • Can I stream data directly into and 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?
  • Does Delta Lake support multi-table transactions?
  • How can I change the type of a column?
• Releases
  • Release notes
  • Compatibility with Apache Spark
• Delta Lake resources
  • Blog posts and talks
  • VLDB 2020 paper
  • Examples
  • Delta Lake transaction log specification
• Optimizations
  • Optimize performance with file management
  • Auto compaction
  • Data skipping
  • Z-Ordering (multi-dimensional clustering)
  • Multi-part checkpointing
  • Log compactions
  • Optimized Write
• Delta table properties reference