Delta Lake vs Apache Iceberg

Developers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution meets developers should learn apache iceberg when building or modernizing data lakes to handle complex analytics, as it addresses common pain points like data consistency, schema changes, and performance at scale. Here's our take.

Delta Lake

Nice Pick

Developers should use Delta Lake when building data pipelines that require reliable, high-quality data with features like data versioning, rollback capabilities, and schema evolution

Pros

• It is particularly valuable for scenarios involving streaming and batch data processing, machine learning workflows, and data lakehouse architectures where combining the scalability of data lakes with the reliability of data warehouses is essential
• Related to: apache-spark, data-lake

Cons

• Specific tradeoffs depend on your use case

Apache Iceberg

Developers should learn Apache Iceberg when building or modernizing data lakes to handle complex analytics, as it addresses common pain points like data consistency, schema changes, and performance at scale

Pros

• It is particularly useful for use cases requiring reliable ETL/ELT pipelines, real-time analytics, and multi-engine access (e
• Related to: apache-spark, apache-hive

Cons

• Specific tradeoffs depend on your use case

The Verdict

These tools serve different purposes. Delta Lake is a platform while Apache Iceberg is a database. We picked Delta Lake based on overall popularity, but your choice depends on what you're building.

The Bottom Line

Delta Lake wins

Based on overall popularity. Delta Lake is more widely used, but Apache Iceberg excels in its own space.