Iceberg vs Delta: Comparing Open Data Table Formats for Modern Data Lake

Blogs

Understanding Object Storage
January 2, 2025
Mastering Data Wrangling: The Essential Foundation of Data Analytics
January 2, 2025

Iceberg vs Delta: Comparing Open Data Table Formats for Modern Data Lake

In the evolving world of big data, managing data lakes efficiently is a challenge that organizations face as they scale their data ecosystems. Open table formats like Apache Iceberg and Delta Lake have emerged as transformative solutions to manage these lakes, enabling organizations to streamline data management, improve performance, and ensure governance.

This blog will compare Iceberg and Delta across several dimensions, helping you choose the best fit for your data lake requirements.

What Are Iceberg and Delta?

Apache Iceberg is an open table format designed for handling large-scale datasets on cloud storage. It was developed to solve inefficiencies in traditional data lakes, ensuring better schema evolution, ACID transactions, and data versioning. It’s widely used in big data tools such as Apache Spark, Flink, and Trino.

Delta Lake is an open-source storage layer that brings reliability to data lakes. Developed by Databricks, Delta Lake offers ACID transactions, scalable metadata handling, and time travel. While initially tightly integrated with Apache Spark, it has since expanded to other platforms.

Key Features Comparison

Key Strengths

Apache Iceberg offers hidden partitioning, which simplifies queries by abstracting partitioning logic. Its rich API is designed for a broad array of tools and engines, making it versatile, and its scalable metadata ensures performance as datasets grow to billions of records.

Delta Lake’s unified support for batch and streaming processing ensures seamless data handling. Its log-based time travel feature allows detailed auditing and rollback capabilities. Additionally, the strong support from Databricks ensures tight integration with Spark, enhancing its ecosystem appeal.

Performance Considerations

Both Iceberg and Delta Lake offer significant performance improvements over traditional data lakes. Iceberg’s hidden partitioning and optimized metadata pruning reduce query latency, especially for large datasets. On the other hand, Delta Lake’s data skipping and Z-order indexing enhance performance in Spark-centric workflows.

Community and Ecosystem

Iceberg is governed by the Apache Software Foundation, ensuring an open and diverse development community. Its integrations with multiple engines make it ideal for organizations using varied tools.

Delta Lake, though open-source, benefits from Databricks’ stewardship, leading to faster innovation. However, its ecosystem has historically been more Spark-focused, though this is changing with broader support.

Use Cases

Apache Iceberg is suited for organizations with multi-engine ecosystems and scenarios requiring complex schema evolution and hidden partitioning.

Delta Lake works well for companies deeply invested in the Databricks ecosystem and use cases requiring seamless batch and streaming operations.

Conclusion

Choosing between Apache Iceberg and Delta Lake depends on your specific needs and existing infrastructure. Iceberg’s flexibility and engine-agnostic design make it ideal for diverse ecosystems, while Delta Lake’s tight integration with Spark and Databricks offers unmatched simplicity for those within that ecosystem.


Geetha S

Leave a Reply

Your email address will not be published. Required fields are marked *