Delta Lake is an open-source storage framework that brings ACID transactions, scalable metadata handling, and time travel to cloud data lakes. Built on top of Parquet, it provides a transactional layer through an append-only commit log (_delta_log) that records every change, enabling reliable concurrent writes and schema evolution without sacrificing performance. Originally developed by Databricks and now a Linux Foundation project, Delta Lake bridges the reliability gap between data warehouses and data lakes, making it the foundation for modern lakehouse architectures on AWS S3, Azure ADLS, and Google Cloud Storage. As of Delta Lake 4.2 (April 2026), the project supports catalog-managed tables, the Variant data type for semi-structured data, and universal format interoperability with Iceberg and Hudi.
What This Cheat Sheet Covers
This topic spans 17 focused tables and 129 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.
Table 1: Core Concepts
| Concept | Example | Description |
|---|---|---|
_delta_log/00000000000000000000.json | • Append-only JSON log that records every table change • each commit creates a new numbered log file, enabling ACID guarantees and time travel | |
Multiple writers commit simultaneously | • Atomicity, Consistency, Isolation, Durability guarantees via optimistic concurrency control • failed transactions roll back without affecting committed data | |
part-00000-<uuid>.snappy.parquet | • Columnar storage format containing actual data • Delta adds metadata layer on top for transactions and versioning | |
_delta_log/00000000000000000010.checkpoint.parquet | • Parquet snapshot of table state written every 10 commits (default) • accelerates metadata reads by avoiding replay of thousands of JSON log entries | |
minReaderVersion=3, minWriterVersion=7 | • Protocol defines minimum client capabilities required to read/write a table • higher versions unlock features like deletion vectors and column mapping |