How does Azure Delta Lake work?

Delta Lake is an open-source storage layer that brings ACID transactions, scalable metadata handling, time travel, schema enforcement and evolution, and unified batch and streaming processing to data lakes. Delta Lake is compatible with Apache Spark and can be used on Azure Databricks and Azure Synapse Analytics.

ACID transactions are a set of properties that ensure data integrity and reliability in database systems. ACID stands for atomicity, consistency, isolation, and durability. Atomicity means that a transaction is either completed entirely or not at all. Consistency means that a transaction does not violate any rules or constraints on the data. Isolation means that a transaction does not interfere with other concurrent transactions. Durability means that a transaction's effects are permanent and do not get lost in case of a failure.

Delta Lake stores data in Parquet format, which is a columnar storage format that supports efficient compression and encoding schemes. Delta Lake also maintains a transaction log that records every change made to the data, enabling data versioning, rollbacks, and audits. Delta Lake allows users to query previous versions of a table using time travel syntax.

Delta Lake supports various APIs for manipulating data, such as Scala, Python, .NET, and SQL. Users can perform merge, update, and delete operations on Delta tables, as well as ingest data from various sources using Delta Live Tables, COPY INTO, Auto Loader, or third-party partners. Delta Lake also supports schema evolution, which means users can add new columns or change the data type of existing columns without breaking existing queries.

Delta Lake is optimized for Structured Streaming on Azure Databricks and Azure Synapse Analytics. Users can use Delta tables as both a source and a sink for streaming workloads, enabling incremental processing at scale. Delta Lake also provides features such as change data feed and idempotent writes to handle streaming scenarios.

Delta Lake is a powerful solution for building reliable and performant data lakes on Azure. It leverages the capabilities of Apache Spark and integrates with other Azure services to provide a lakehouse architecture that combines the best of data warehouses and data lakes.