This blog post provides an introductory guide to achieving Change Data Capture (CDC) with Postgres and ClickHouse. The solution proposed uses only native features of ClickHouse, without requiring additional components beyond Debezium and Kafka. The approach is push-based, where the source database captures changes and sends them to the target system in near real-time. To achieve this, PostgreSQL's Write-Ahead Log (WAL) and logical decoding are exploited, along with the open-source tool Debezium. Debezium produces row-level change events that can be sent to Kafka for consumption by downstream sinks. The ReplacingMergeTree table engine is used in ClickHouse to efficiently handle updates and deletes, while ensuring data consistency and query performance. The blog post also discusses important considerations for using this CDC pipeline, including partitioning and filtering on primary key columns to improve performance.