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  1. delta.ioHome | Delta Lake

    Delta Lake is an open-source storage framework that enables building a format agnostic Lakehouse architecture with compute engines including Spark, PrestoDB, Flink, Trino, Hive, Snowflake, Google BigQuery, Athena, Redshift, Databricks, Azure Fabric and APIs for Scala, Java, Rust, and Python.

    • Tutorials

      Module 1: Delta Lake 1.2 Tutorial with Jacek Laskowski...

    • Videos

      Delta Lake Connector for Presto - Denny Lee, Databricks...

    • Introduction

      Delta Lake is an open source project that enables building a...

    • Quickstart

      Quickstart. This guide helps you quickly explore the main...

  2. docs.databricks.com › en › deltaWhat is Delta Lake? | Databricks on AWS

    May 16, 2024. 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.

  3. learn.microsoft.com › en-us › azureWhat is Delta Lake? - Azure Databricks | Microsoft Learn

    • Overview
    • Getting started with Delta Lake
    • Converting and ingesting data to Delta Lake
    • Updating and modifying Delta Lake tables
    • Incremental and streaming workloads on Delta Lake
    • Querying previous versions of a table
    • Delta Lake schema enhancements
    • Managing files and indexing data with Delta Lake
    • Configuring and reviewing Delta Lake settings
    • Data pipelines using Delta Lake and Delta Live Tables

    Delta Lake is the optimized storage layer that provides the foundation for storing data and tables in the Databricks lakehouse. 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 storage 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.

    See full list on learn.microsoft.com

    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.

    See full list on learn.microsoft.com

    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

    See full list on learn.microsoft.com

    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.

    •Delta Lake provides numerous options for selective overwrites based on filters and partitions.

    •You can manually or automatically update your table schema without rewriting data.

    See full list on learn.microsoft.com

    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

    •Enable idempotent writes across jobs

    See full list on learn.microsoft.com

    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.

    See full list on learn.microsoft.com

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

    •Delta Lake schema validation

    •Constraints on Azure Databricks

    •Use Delta Lake generated columns

    See full list on learn.microsoft.com

    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

    •Compact data files with optimize on Delta Lake

    •Remove unused data files with vacuum

    •Configure Delta Lake to control data file size

    See full list on learn.microsoft.com

    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

    See full list on learn.microsoft.com

    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.

    See full list on learn.microsoft.com

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