Delta Lake is an open-source storage and processing framework developed by Databricks. It is designed to simplify data management and data processing in large data lakes, providing both transaction reliability and the scalability of big data processing.
Delta Lake is built on top of Apache Spark and leverages the power of Spark’s distributed processing capabilities. However, it adds some additional features and capabilities to improve data reliability and data integrity. Key features of Delta Lake include:
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ACID transactions: Delta Lake provides support for ACID (Atomicity, Consistency, Isolation, Durability) transactions. This allows multiple operations to be performed in parallel on data without conflicts or inconsistencies.
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Schema evolution: Delta Lake supports schema evolution, allowing users to easily make changes to the data schema without having to delete or reprocess the existing data.
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Data versioning: Delta Lake provides data versioning, allowing users to access previous versions of the data and easily switch between different versions. This is useful for auditing purposes, time travel queries and rolling back unwanted changes.
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Time travel: Delta Lake allows users to go back in time and restore historical snapshots of data. This allows analysis to be performed on previous states of the data, which is useful in comparing trends, debugging problems and restoring data to a particular point in time.
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Upserts and deletes: Delta Lake provides the ability to perform upserts (updates and inserts) and deletes on data. This allows users to update records, add new records and delete records in a Delta Lake table.
Delta Lake frameworks popular in Big data world
Delta Lake makes it easier for organizations to build and manage reliable and scalable data lakes, with built-in data integrity, data versioning and data control mechanisms. It has become popular in the big data world because of its features and compatibility with Apache Spark, making it widely used in various data-intensive applications and analytics scenarios.
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