Change Data Capture (CDC) Analytics Pipeline

Overview

Designed and implemented a CDC-style analytics pipeline to reliably capture insert, update, and delete events from an OLTP system and materialize them into analytics-ready warehouse tables. The pipeline supports replayability, fault tolerance, and historical correctness, enabling near real-time analytics without sacrificing data integrity.

Challenge

CDC systems must handle out-of-order events, late-arriving updates, retries, and deletes while maintaining a consistent downstream state. Naive ingestion approaches can lead to duplicated records, missed deletes, or broken history. The challenge was to build a system that remained correct under replays and failures while supporting scalable analytics workloads.

Solution

Built a fault-tolerant CDC architecture with the following design:

• Event ingestion: Insert, update, and delete events are published to Kafka and consumed with checkpointing to support replayability and exactly-once semantics at the analytics layer.
• Bronze → Silver → Gold modeling: Raw CDC events are staged, deduplicated, and transformed into analytics-ready tables with preserved historical state where required.
• Idempotent processing: Primary-key-aware deduplication and deterministic MERGE logic prevent duplication during retries and reprocessing.
• History preservation: Slowly Changing Dimension (SCD Type-2) models maintain a full record of state changes for customer and subscription analytics.
• Observability: Pipeline lag, freshness, and failure modes are monitored to ensure reliable downstream consumption.

Technical Implementation

Core components and tooling used:

• Apache Kafka for durable CDC event transport
• Python-based consumers for ingestion, validation, and staging
• Google BigQuery as the analytical warehouse
• dbt incremental models using MERGE semantics and SCD Type-2 dimensions
• Apache Airflow for orchestration, monitoring, and replay workflows
• Cloud Run / Cloud Functions for scalable, stateless processing