A small team with PostgreSQL 9.2—unsupported since 2017—faced a 15TB database and needed near real-time ingestion to Snowflake without a costly upgrade. The author devised a workaround using database triggers and Kafka Connect since modern CDC tools like Debezium require newer PostgreSQL versions. This approach captures changes in a custom log table, bypassing the need for a full database migration.

The solution registers a JDBC Source Connector in Kafka to poll the `transaction_cdc_log` table every second. A Python client then consumes these events and streams them to Snowpipe Streaming in batches. Using MERGE operations, the client efficiently upserts data into Snowflake, achieving sub-10-second latency for 5 transactions per second in testing. This demonstrates a practical path for legacy system integration.

For production, the author notes scalability and observability as key considerations, suggesting monitoring for lag and error rates. The approach requires manual trigger setup per table, which could be automated. While not ideal, this method proves viable for constrained teams, highlighting a common enterprise challenge: modernizing data pipelines with minimal resources and budget.