The CDC pipeline for Postgres to ClickHouse uses Debezium, Kafka Connect, and a materialized view to capture changes in near real-time. The architecture assumes the user has an instance of Kafka with the Kafka Connect framework, where the connector is configured to track changes at a per table level. The configuration includes settings such as `max.batch.size`, `poll.interval.ms`, and `consumer.override.max.poll.records` to optimize batching and polling intervals. Additionally, users should be aware of other considerations, including WAL disk usage, heartbeat interval, primary key changes, DDL changes, and logical decoding replication slots. The pipeline is designed for production use cases and can be scaled at a table level using a connector per table approach. Users are encouraged to explore other databases supported by Debezium and share their experiences.