ClickHouse Unleashes Data Lake Speed

Bridging the Lake and Real-Time Analytics

ClickHouse's 'data lake ready' announcement is a compelling move, directly tackling the performance bottleneck often associated with open table formats like Iceberg and Delta Lake. The two-year engineering effort is evident, particularly in the detailed roadmap and the comprehensive support for Parquet, various catalogs, and cloud storage. The ability to query data in place without movement offers significant flexibility and reduces data duplication, while the option to load data into ClickHouse's native MergeTree engine for accelerated analytics provides a dual-pronged approach to satisfy different performance needs. This hybrid strategy is a major strength, allowing users to leverage the openness of data lakes while still achieving sub-second query performance for critical workloads. The expanded catalog support, aiming for catalog-agnosticism, is also a smart move in a fragmented ecosystem, reducing vendor lock-in for data management.

However, a key concern for potential adopters will be the maturity and stability of these new features, especially given that Iceberg support is still noted as 'beta' in one instance. While the article highlights impressive performance gains, real-world performance will heavily depend on the specific data structures, query patterns, and underlying cloud infrastructure. The initial setup for catalog integration, particularly with cloud-specific credentials, might also present a learning curve for some users. Furthermore, while ClickHouse aims for interoperability by writing results back to open formats, the article doesn't delve deeply into the mechanics or potential performance implications of these write operations compared to native ClickHouse writes. The success of this initiative will ultimately hinge on continued investment in performance optimization, robust error handling, and clear documentation for a seamless user experience across diverse data lake environments.

Key Points

• ClickHouse now directly supports querying data in place from data lakes using open table formats like Apache Iceberg and Delta Lake.
• Offers two primary modes: querying data directly on the lake for exploration, and loading data into ClickHouse's native MergeTree engine for sub-second, high-concurrency real-time analytics.
• Significant engineering effort over two years has resulted in enhanced Parquet processing, broad catalog integration (Unity Catalog, AWS Glue, Polaris, etc.), and expanded cloud storage support (S3, GCS, Azure Blob Storage, OneLake).
• Key technical advancements include native Parquet reader with page-level parallelism, Delta Rust Kernel integration, and optimizations like row group skipping and predicate-level caching.
• The solution is designed to be cloud-agnostic and catalog-agnostic, providing flexibility and avoiding vendor lock-in.
• Users can perform interactive DML operations (insert, delete, update, alter schema) directly on Iceberg tables and benefit from features like time travel for Iceberg.

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📖 Source: ClickHouse is data lake ready