Change Data Capture Pipeline with Kafka
I built a real-time CDC pipeline using Debezium and Kafka to stream database changes to downstream systems, enabling real-time analytics and data synchronization.
At a glance
- •Latency: 24 hours → <1 second
- •95% fewer overselling incidents
- •500K+ events/day with 10x headroom
Problem Statement
An e-commerce platform needed to sync data changes from their transactional PostgreSQL database to a data warehouse and search indexes in real-time. Their batch ETL was causing delays of up to 24 hours, which hurt both business decisions and customer experience.
Architecture & Design
I designed a CDC pipeline using Debezium to capture row-level changes from PostgreSQL's logical replication slot. Kafka serves as the central streaming platform with topics organized by database tables. Kafka Connect sinks write changes to Elasticsearch for search and Snowflake for analytics. Schema Registry keeps data contracts enforced.
Implementation Details
I configured the Debezium PostgreSQL connector with snapshot mode for initial data load and streaming mode for ongoing changes. Built custom message transformations for data enrichment and filtering. Wrote Python consumers for complex transformations that Kafka Connect couldn't handle. Set up monitoring for replication slot lag and consumer group lag.
Results & Outcome
Data latency dropped from 24 hours to under 1 second. Real-time inventory updates in search results cut overselling incidents by 95%. The data warehouse stayed fresh for BI dashboards. The system handles 500K+ events per day with headroom for 10x scale.