What is the most efficient way to handle Change Data Capture (CDC) from Postgres to Snowflake?

All answers to this question.

For near real-time data, Debezium is the gold standard because it reads from the Postgres WAL (Write Ahead Log) without putting load on the database. However, running a Kafka cluster can be a full-time job. If your team is mid-sized, I recommend looking at managed CDC services like Fivetran or Airbyte (Cloud version). They use the same log-based approach but handle the orchestration and schema mapping for you. In a 2024 comparison we did, Fivetran had higher costs but saved us approximately 15 engineering hours per week in maintenance. If you must go the Kafka route, use a managed provider like Confluent to at least remove the infrastructure burden, as configuring Kafka Connect for Snowflake can be quite tricky.

   Answered 2025-12-15 by Margaret Moore

Does using CDC on the production database cause any significant performance degradation during high-traffic periods?

   Answered 2025-12-28 by William Taylor

• William, log-based CDC (like Debezium) has a very minimal footprint—usually less than 3% CPU overhead—because it's just reading a file the database is already writing. This is far better than "Query-based" CDC, which has to constantly scan your tables for updated timestamps. Just make sure your log retention period is long enough so that if your consumer goes down, you don't lose the data changes before they are synced to Snowflake.

     Commented 2025-12-29 by Richard Garcia

We implemented "Snowpipe" in combination with S3. Our CDC tool writes the changes to S3, and Snowpipe ingests them into Snowflake the second they land.

   Answered 2025-12-30 by Elizabeth Clark

• Snowpipe is a great addition, Elizabeth. It really helps keep the Snowflake costs down by avoiding the need for a running warehouse to handle those frequent, small ingestion increments.

     Commented 2025-12-31 by Robert Anderson