Postgres to ClickHouse: Data Modeling Tips V2

Replicating data from PostgreSQL to ClickHouse using Postgres CDC can be an efficient way to scale business, enabling real-time analysis of large datasets. To achieve this, customers often use Change Data Capture (CDC) to continuously track inserts, updates, and deletes in Postgres and replicate them to ClickHouse. This allows for real-time analytics and reduces the load on PostgreSQL. However, managing duplicate rows and optimizing query performance is crucial. Strategies such as using Refreshable Materialized Views, denormalization, and custom ordering keys can help with deduplication, JOINs queries, and query optimization. By leveraging these best practices, customers can maximize ClickHouse's speed and scalability, and get the most out of Postgres CDC integration.

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