Clickhouse ® Integration HDFS for Sub-100ms Lake Queries

The integration of Clickhouse® with HDFS addresses distinct challenges for teams using Hadoop data lakes by enabling direct querying of files in HDFS, loading data into MergeTree tables for low-latency serving, continuous ingestion, and utilizing HDFS as a remote storage tier. This integration is significant as enterprise data lakes remain predominantly on HDFS, despite cloud migration trends. The combination leverages HDFS for distributed storage and Clickhouse® for superior analytical query performance, offering solutions such as federated queries, staging to serving, lakehouse integration, and tiered storage. Key integration patterns include direct queries for exploratory analysis, parallel reads for large datasets, Hive/Iceberg integration for lakehouse compatibility, and loading to MergeTree for production analytics. The document emphasizes the importance of understanding HDFS's architecture, optimal file handling, and performance considerations to maximize the effectiveness of this integration. It highlights that while HDFS is optimized for durable storage and batch processing, Clickhouse® enhances real-time analytical capabilities, thus offering a balanced approach to managing lakehouse data with both durability and low-latency query performance.

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