Delta Lake Basics


 Delta Lake Basics

Delta Lake is an open-source storage layer that brings ACID transactions to Apache Spark and big data workloads. It runs on top of existing data lakes (like Azure Data Lake, S3, or HDFS) and enhances them with reliability, consistency, and performance. Delta Lake solves common problems in traditional data lakes, such as data corruption, schema mismatches, and inconsistent reads.

One of the key features of Delta Lake is ACID transactions, which ensure that read and write operations are reliable even in concurrent environments. This is achieved through a transaction log called the Delta Log, which records every change (like updates, deletes, merges) made to the table.

Delta Lake supports schema enforcement and schema evolution, which means it can prevent incorrect data from being written or automatically adjust to new columns. It also enables time travel, allowing users to query previous versions of data using a timestamp or version number.

With Delta Lake, batch and streaming data can be processed in the same pipeline using unified batch and streaming capabilities. It also supports data compaction (OPTIMIZE) and vacuuming to manage performance and storage cleanup.

Overall, Delta Lake brings data reliability, fast performance, and streamlined data engineering workflows to big data platforms like Databricks, making it ideal for building robust data lakes and lakehouses.

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