delta-io / delta

An open-source storage layer that brings scalable, ACID transactions to Apache Spark™ and big data workloads.

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Delta Lake is a storage layer that brings scalable, ACID transactions to Apache Spark and other big-data engines.

See the Delta Lake Documentation for details.

See the Quick Start Guide to get started with Scala, Java and Python.

Latest Binaries

Delta Lake is published to Maven Central Repository and can be used by adding a dependency in your POM file.



Compatibility with Apache Spark Versions

Delta Lake currently requires Apache Spark 2.4.2. Earlier versions are missing SPARK-27453, which breaks the partitionBy clause of the DataFrameWriter.

API Compatibility

The only stable, public APIs currently provided by Delta Lake are through the DataFrameReader/Writer (i.e., df.write, spark.readStream and df.writeStream). Options to these APIs will remain stable within a major release of Delta Lake (e.g., 1.x.x).

All other interfaces in the this library are considered internal, and they are subject to change across minor / patch releases.

Data Storage Compatibility

Delta Lake guarantees backward compatibility for all Delta Lake tables (i.e., newer versions of Delta Lake will always be able to read tables written by older versions of Delta Lake). However, we reserve the right to break forwards compatibility as new features are introduced to the transaction protocol (i.e., an older version of Delta Lake may not be able to read a table produced by a newer version).

Breaking changes in the protocol are indicated by incrementing the minimum reader/writer version in the Protocol action.


Delta Lake is a recent open source project based on technology developed at Databricks. We plan to open-source all APIs that are required to correctly run Spark programs that read and write Delta tables. For a detailed timeline on this effort see the project roadmap.


Delta Lake Core is compiled using SBT.

To compile, run

build/sbt compile

To generate artifacts, run

build/sbt package

To execute tests, run

build/sbt test

Refer to SBT docs for more commands.

Transaction Protocol

Delta Lake works by storing a transaction log along side the data files in a table. Entries in the log, called delta files, are stored as atomic collections of actions in the _delta_log directory, at the root of a table. Entries in the log encoded using JSON and are named as zero-padded contiguous integers.


To avoid needing to read the entire transaction log every time a table is loaded, Delta Lake also occasionally creates a checkpoint, which contains the entire state of the table at the given version. Checkpoints are encoded using Parquet and must only be written after the accompanying Delta Lake files have been written.

Requirements for Underlying Storage Systems

Delta Lake ACID guarantees are predicated on the atomicity and durability guarantees of the storage system. Specifically, we require the storage system to provide the following.

  1. Atomic visibility: There must be a way for a file to be visible in its entirety or not visible at all.
  2. Mutual exclusion: Only one writer must be able to create (or rename) a file at the final destination.
  3. Consistent listing: Once a file has been written in a directory, all future listings for that directory must return that file.

Given that storage systems do not necessarily provide all of these guarantees out-of-the-box, Delta Lake transactional operations typically go through the LogStore API instead of accessing the storage system directly. We can plug in custom LogStore implementations in order to provide the above guarantees for different storage systems. Delta Lake has built-in LogStore implementations for HDFS, Amazon S3 and Azure storage services. Please see Delta Lake Storage Configuration for more details. If you are interested in adding a custom LogStore implementation for your storage system, you can start discussions in the community mailing group.

As an optimization, storage systems can also allow partial listing of a directory, given a start marker. Delta Lake can use this ability to efficiently discover the latest version of a table, without listing all of the files in the transaction log.

Concurrency Control

Delta Lake ensures serializability for concurrent reads and writes. Please see Delta Lake Concurrency Control for more details.

Reporting issues

We use GitHub Issues to track community reported issues. You can also contact the community for getting answers.


We welcome contributions to Delta Lake. We use GitHub Pull Requests for accepting changes. You will be prompted to sign a contributor license agreement before your change can be accepted.


There are two mediums of communication within the Delta Lake community.