Open Table Formats Explained: Iceberg vs. Delta Lake vs. Hudi
This overview details and compares Apache Iceberg, Delta Lake, and Apache Hudi, the three major open table formats that bring ACID transactions, schema evolution, and time travel to data lakes. These formats transform collections of files in object storage into reliable, queryable tables, addressing traditional data lake consistency issues and enabling database-like features. The article explains their internal mechanisms, contrasts their design histories and performance characteristics for updates, and highlights how features like catalog-coordinated transactions are leading to convergence in capabilities. It helps architects understand the trade-offs and interoperability considerations when choosing a table format for a modern data lakehouse architecture.
- →Understanding Open Table Formats for Data Lakes
- →Overview of Apache Iceberg, Delta Lake, and Apache Hudi
- →Comparing Key Capabilities Across Open Table Formats
- →Apache Iceberg's Metadata Management and Time Travel
- →Delta Lake's ACID Transactions and Multi-Engine Support
Notes (6) ›
- Understanding Open Table Formats for Data Lakes
Open table formats are metadata layers atop object storage, enabling ACID transactions, schema evolution, and time travel for data lakes. They transform raw file collections into reliable, queryable tables, combining data lake cost-efficiency with data warehouse reliability.
- Overview of Apache Iceberg, Delta Lake, and Apache Hudi
Apache Iceberg, Delta Lake, and Apache Hudi are the three major open table formats, each built upon columnar file formats like Parquet or ORC. The article details their distinct origins and core mechanisms for managing data files.
- Comparing Key Capabilities Across Open Table Formats
The article provides a quick comparison across formats, highlighting differences in ACID transactions, schema evolution, partition evolution, update/delete performance, and multi-engine support. It notes the increasing convergence of features between them.
- Apache Iceberg's Metadata Management and Time Travel
Apache Iceberg uses a layered metadata tree to efficiently track data files, enabling performance optimizations and time travel through immutable snapshots. It supports schema and partition evolution without rewriting underlying files.
- Delta Lake's ACID Transactions and Multi-Engine Support
Delta Lake ensures ACID properties for data lakes through a write-ahead transaction log, providing consistent views even during concurrent writes. Originally Spark-native, it now offers broad multi-engine support via independent connectors and UniForm.
- Interoperability and Vendor Lock-in Mitigation
The article discusses options for catalog interoperability and features like Delta Lake UniForm that reduce vendor lock-in. It highlights how these innovations allow querying data natively as Delta Lake or Iceberg across various engines.
https://www.databricks.com/blog/open-table-formats
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