Integrating ClickHouse into a data pipeline starts with defining it as the main analytical store and designing schemas around your access patterns using MergeTree engines, partitions, and sorting keys. Source data can be ingested from logs, applications, and transactional databases via Kafka, change data capture tools, streaming platforms, or batch ETL jobs that write to ClickHouse using the native, HTTP, or JDBC/ODBC interfaces. A common pattern is to land raw events in staging tables, then transform them into curated aggregates or dimensional models using SQL, materialized views, and scheduled jobs orchestrated by tools like Airflow or other workflow engines. You should also integrate monitoring, alerting, and backup processes so that ingestion performance, query latency, disk growth, and replication health are continuously tracked, ensuring the pipeline remains reliable and scalable in production.