Blog

Apache Iceberg is an open table format that offers ACID transactions and schema evolution, making it a preferred choice for large-scale analytics, while ClickHouse is a standard for real-time analytics. Integrating these two allows direct querying of Iceberg tables from ClickHouse, eliminating the need for complex ETL processes. However, achieving optimal performance requires understanding ClickHouse's interaction with Iceberg metadata, selecting appropriate table functions and engines, and optimizing table structure for query latency. The guide discusses various strategies, such as partitioning, sorting, and using materialized views, to enhance performance. It advises a hybrid approach, where Iceberg serves as the data lake and source of truth, while frequently accessed data is copied into ClickHouse for fast real-time queries. This combination leverages Iceberg's schema flexibility and ClickHouse's query speed but involves tradeoffs, particularly in balancing data freshness against query performance.