A Near-Real-Time CDC Engine for Snowflake — Built, Not Bought
A purpose-built change data capture engine replacing commercial CDC tooling across five heterogeneous source systems — 8 million events per day at baseline, peaks over 40 million, end-to-end latency consistently under two minutes.
Most Snowflake-using organizations end up buying several third-party systems to move data in and out securely. This engagement demonstrates the alternative: a purposefully-built ingestion layer that replaced the commercial CDC stack at a fraction of the licensing cost — while handling the edge cases the off-the-shelf tools cannot.
The full write-up will cover:
- The ordering, idempotency, and schema-evolution problems every CDC system must solve — and where vendors cut corners
- Why five heterogeneous source systems break most commercial tooling
- Operational design: monitoring, replay, backfill, and failure recovery
- The build-vs-buy economics of data ingestion at petabyte scale
- A twenty-year lineage: this is the same architectural instinct as bypassing Apache vhost limits in 2001, with different tools
Full case study coming soon.