![]() ![]() In short, Delta tables is a data table architecture while Delta Live Tables is a data pipeline framework. Delta Live Tables is a declarative framework that manages many delta tables, by creating them and keeping them up to date. Delta Live Tablesĭelta table is a way to store data in tables, whereas Delta Live Tables allows you to describe how data flows between these tables declaratively. Delta Live Tables Enhanced Autoscaling can handle streaming workloads which are spiky and unpredictable. You define the transformations to perform on your data, and Delta Live Tables manages task orchestration, cluster management, monitoring, data quality, and error handling. Users can perform both batch and streaming operations on the same table and the data is immediately available for querying. Delta Live Tables offers declarative pipeline development, improved data reliability, and cloud-scale production operations. The pipeline is the main unit of execution for Delta Live Tables. ![]() DeltaTable class: Main class for interacting programmatically with Delta tables.ĭelta Live Tables manage the flow of data between many Delta tables, thus simplifying the work of data engineers on ETL development and management.Updating and modifying Delta Lake tables.Delta tables are typically used for data lakes, where data is ingested via streaming or in large batches. Delta tables: Default data table architectureĭelta table is the default data table format in Azure Databricks and is a feature of the Delta Lake open source data framework. It allows you to handle both batch and streaming data in a unified way.ĭelta tables are built on top of this storage layer and provide a table abstraction, making it easy to work with large-scale structured data using SQL and the DataFrame API. It allows for ACID transactions, data versioning, and rollback capabilities. Delta Lake: OS data management for the lakehouseĭelta Lake is an open-source storage layer that brings reliability to data lakes by adding a transactional storage layer on top of data stored in cloud storage (on AWS S3, Azure Storage, and GCS). Delta Lake was conceived of as a unified data management system for handling transactional real-time and batch big data, by extending Parquet data files with a file-based transaction log for ACID transactions and scalable metadata handling. How are they related to and distinct from one another?ĭelta is a term introduced with Delta Lake, the foundation for storing data and tables in the Databricks lakehouse.What do they do? Or what are they used for?.What are the Delta technologies in Azure Databricks?.Delta refers to technologies related to or in the Delta Lake open source project. This article is an introduction to the technologies collectively branded Delta on Azure Databricks. ![]()
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