What is Delta Lake?

Delta Lake is the optimized storage layer that provides the foundation for tables in a lakehouse on Databricks. Delta Lake is open source software that extends Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling. Delta Lake is fully compatible with Apache Spark APIs, and was developed for tight integration with Structured Streaming, allowing you to easily use a single copy of data for both batch and streaming operations and providing incremental processing at scale.

Delta Lake is the default format for all operations on Azure Databricks. Unless otherwise specified, all tables on Azure Databricks are Delta tables. Databricks originally developed the Delta Lake protocol and continues to actively contribute to the open source project. Many of the optimizations and products in the Databricks platform build upon the guarantees provided by Apache Spark and Delta Lake. For information on optimizations on Azure Databricks, see Optimization recommendations on Azure Databricks.

For reference information on Delta Lake SQL commands, see Delta Lake statements.

The Delta Lake transaction log has a well-defined open protocol that can be used by any system to read the log. See Delta Transaction Log Protocol.

Getting started with Delta Lake

All tables on Azure Databricks are Delta tables by default. Whether you’re using Apache Spark DataFrames or SQL, you get all the benefits of Delta Lake just by saving your data to the lakehouse with default settings.

For examples of basic Delta Lake operations such as creating tables, reading, writing, and updating data, see Tutorial: Delta Lake.

Databricks has many recommendations for best practices for Delta Lake.

Converting and ingesting data to Delta Lake

Azure Databricks provides a number of products to accelerate and simplify loading data to your lakehouse.

• Delta Live Tables:
  • Tutorial: Run your first ETL workload on Databricks
  • Load data using streaming tables (Python/SQL notebook)
  • Load data using streaming tables in Databricks SQL
• COPY INTO
• Auto Loader
• Add data UI
• Incrementally convert Parquet or Iceberg data to Delta Lake
• One-time conversion of Parquet or Iceberg data to Delta Lake
• Third-party partners

For a full list of ingestion options, see Ingest data into a Databricks lakehouse.

Updating and modifying Delta Lake tables

Atomic transactions with Delta Lake provide many options for updating data and metadata. Databricks recommends you avoid interacting directly with data and transaction log files in Delta Lake file directories to avoid corrupting your tables.

• Delta Lake supports upserts using the merge operation. See Upsert into a Delta Lake table using merge.
• Delta Lake provides numerous options for selective overwrites based on filters and partitions. See Selectively overwrite data with Delta Lake.
• You can manually or automatically update your table schema without rewriting data. See Update Delta Lake table schema.
• Enable columns mapping to rename or delete columns without rewriting data. See Rename and drop columns with Delta Lake column mapping.

Incremental and streaming workloads on Delta Lake

Delta Lake is optimized for Structured Streaming on Azure Databricks. Delta Live Tables extends native capabilities with simplified infrastructure deployment, enhanced scaling, and managed data dependencies.

• Delta table streaming reads and writes
• Use Delta Lake change data feed on Azure Databricks

Querying previous versions of a table

Each write to a Delta table creates a new table version. You can use the transaction log to review modifications to your table and query previous table versions. See Work with Delta Lake table history.

Delta Lake schema enhancements

Delta Lake validates schema on write, ensuring that all data written to a table matches the requirements you’ve set.

• Schema enforcement
• Constraints on Azure Databricks
• Delta Lake generated columns
• Enrich Delta Lake tables with custom metadata

Managing files and indexing data with Delta Lake

Azure Databricks sets many default parameters for Delta Lake that impact the size of data files and number of table versions that are retained in history. Delta Lake uses a combination of metadata parsing and physical data layout to reduce the number of files scanned to fulfill any query.

• Use liquid clustering for Delta tables
• Data skipping for Delta Lake
• Optimize data file layout
• Remove unused data files with vacuum
• Configure Delta Lake to control data file size

Configuring and reviewing Delta Lake settings

Azure Databricks stores all data and metadata for Delta Lake tables in cloud object storage. Many configurations can be set at either the table level or within the Spark session. You can review the details of the Delta table to discover what options are configured.

• Review Delta Lake table details with describe detail
• Delta table properties reference

Data pipelines using Delta Lake and Delta Live Tables

Azure Databricks encourages users to leverage a medallion architecture to process data through a series of tables as data is cleaned and enriched. Delta Live Tables simplifies ETL workloads through optimized execution and automated infrastructure deployment and scaling.

Delta Lake feature compatibility

Not all Delta Lake features are in all versions of Databricks Runtime. For information about Delta Lake versioning, see How does Azure Databricks manage Delta Lake feature compatibility?.

Delta Lake API documentation

For most read and write operations on Delta tables, you can use Spark SQL or Apache Spark DataFrame APIs.

For Delta Lake-specific SQL statements, see Delta Lake statements.

Azure Databricks ensures binary compatibility with Delta Lake APIs in Databricks Runtime. To view the Delta Lake API version packaged in each Databricks Runtime version, see the System environment section on the relevant article in the Databricks Runtime release notes. For documentation on Delta Lake APIs for Python, Scala, and Java, see the OSS Delta Lake documentation.