Real-Time CDC Analytics Pipeline
From operational PostgreSQL changes to analytics-ready layers
The challenge
Analytics teams often need fresher data from operational systems, but many solutions create lock-in, hide transformation logic, or become too heavy for smaller data teams. This project frames CDC as a business capability: move trusted operational changes into analytics quickly and transparently.
How we solved it
- - Capture PostgreSQL changes with Debezium and Kafka Connect
- - Normalize and upsert change events with a Python consumer
- - Model bronze, silver, and gold layers with dbt
- - Expose the pipeline through a lightweight dashboard
Execution story
PostgreSQL emits changes, Kafka transports them, Python normalizes payloads, dbt shapes the warehouse layers, and Streamlit closes the loop for observability.
Why this case matters
Many teams discuss real-time data, but what matters in practice is reliable movement from transactional systems to business-facing analytics. This case shows how CDC can be treated as an operational capability that reduces delay between event creation and decision making.
What an executive should notice
The value is not in Kafka or Debezium alone. The value is in making operational change data available faster, with clear ownership and transparent transformation logic.