Delta Lake

Delta Lake is an open-source storage framework designed to enable the creation of a Lakehouse architecture with various compute engines, including Spark, PrestoDB, Flink, Trino, and Hive. It is positioned as a robust solution for managing and processing large-scale datasets, targeting companies of all sizes, from small startups to large enterprises. Data engineers, data scientists, data analysts, software developers, and professionals in the financial services industry are among the key users who can benefit from Delta Lake's capabilities.

Key Features

ACID Transactions: According to the vendor, Delta Lake provides ACID transactions to ensure data integrity and protect data from concurrent modifications. It guarantees serializability, allowing multiple transactions to read and write data without conflicts.

Scalable Metadata: The vendor claims that Delta Lake can handle petabyte-scale tables with billions of partitions and files. It efficiently manages metadata using a scalable metadata management system, enabling fast and efficient operations on large datasets.

Time Travel: Delta Lake enables users to access and revert to earlier versions of data, facilitating auditing, rollbacks, reproducing past results, and tracking changes over time. Users can query the table as it existed at a specific point in time or retrieve the entire history of changes.

Open Source: Delta Lake is an open-source project driven by the Delta Lake Project under the Linux Foundation. It is built on open standards and protocols, allowing for community contributions, open discussions, and transparency in the development process.

Unified Batch/Streaming: According to the vendor, Delta Lake provides unified batch and streaming data processing capabilities, supporting exactly once semantics for data ingestion. Users can seamlessly transition from batch to interactive queries, enabling real-time analytics and interactive data exploration on the same dataset.

Schema Evolution / Enforcement: Delta Lake supports schema evolution, allowing users to evolve the schema of a table over time. It provides mechanisms to add, modify, and delete columns while maintaining backward compatibility. Schema enforcement ensures data integrity by validating data against the specified schema.

Audit History: Delta Lake logs all changes made to the data, providing a comprehensive audit trail. It captures metadata changes, schema changes, and data changes, including inserts, updates, and deletes. The audit history can be used for compliance, data lineage, and debugging purposes.

DML Operations: Delta Lake supports Data Manipulation Language (DML) operations, allowing users to perform SQL, Scala/Java, and Python-based operations on Delta tables. Users can merge, update, and delete datasets using familiar SQL syntax or programmatic APIs.

Data Skipping: According to the vendor, Delta Lake utilizes data skipping to optimize query performance by automatically skipping irrelevant data based on statistics and filters. This feature aims to significantly improve query execution time, especially for large datasets.

Delta Caching: The vendor claims that Delta Lake provides efficient data caching capabilities, allowing users to cache data in-memory or on disk for faster access and improved query performance. It optimizes data caching based on query patterns and usage patterns, providing an enhanced user experience.