Change Data Capture has evolved from a specialized database replication technique into a fundamental pattern for modern data architectures. Building production-grade CDC pipelines on AWS requires orchestrating multiple services—DMS for change capture, Kinesis or MSK for streaming, Lambda or Glue for transformation, and S3 or data warehouses for storage. The complexity lies not in any … Read more
Change Data Capture has become a popular pattern for data integration, real-time analytics, and event-driven architectures. The ability to track database changes and propagate them to downstream systems sounds universally beneficial. Yet CDC implementations frequently create more problems than they solve when applied inappropriately. Understanding when CDC is the wrong choice saves organizations from architectural … Read more
In the modern data engineering landscape, the combination of Change Data Capture (CDC) and Delta Lake has emerged as a powerful pattern for building reliable, scalable data pipelines. A Delta CDC pipeline captures changes from source systems and writes them to Delta Lake tables, enabling organizations to maintain real-time synchronized data warehouses while preserving complete … Read more
Data lakes have become the cornerstone of modern analytics architectures, consolidating vast amounts of structured and unstructured data in a cost-effective storage layer. However, keeping these lakes fresh with the latest operational data has traditionally relied on batch ETL processes that introduce significant latency—often hours or even days between when data changes occur in source … Read more
Designing a Change Data Capture (CDC) pipeline that reliably delivers incremental data loads requires more than just connecting a CDC tool to your database and hoping for the best. Production-grade CDC pipelines must handle edge cases, maintain consistency during failures, scale with data volume growth, and provide visibility into their operation. The difference between a … Read more
The evolution of data engineering has fundamentally shifted from batch-oriented Extract, Transform, Load (ETL) processes to continuous, event-driven architectures. Change Data Capture (CDC) sits at the heart of this transformation, enabling organizations to move beyond scheduled data transfers to real-time synchronization. Understanding CDC isn’t just about knowing that it captures database changes—it’s about grasping how … Read more
Building your first real-time CDC pipeline can feel overwhelming with the abundance of tools and architectural choices available. This hands-on guide walks through a complete, production-ready example that streams changes from a PostgreSQL database through Kafka to a data warehouse, demonstrating every step from initial setup to monitoring. Rather than abstract concepts, you’ll see actual … Read more
Change Data Capture (CDC) has become a foundational pattern in modern data engineering, yet many practitioners struggle with its nuances and implementation challenges. At its essence, a CDC data pipeline continuously identifies and captures changes made to data in source systems, then propagates those changes to target systems with minimal latency. Unlike traditional batch ETL … Read more
Building a Change Data Capture (CDC) pipeline with Apache Airflow and PostgreSQL creates a powerful data integration solution that balances real-time requirements with operational simplicity. While Airflow is traditionally known for batch orchestration, its extensible architecture and support for sensors, custom operators, and dynamic DAG generation make it surprisingly capable for near real-time CDC workloads. … Read more
Change Data Capture (CDC) pipelines built on Databricks and Delta Lake represent a paradigm shift in how organizations handle real-time data integration. Unlike traditional ETL approaches that rely on scheduled batch processing, a CDC pipeline continuously captures and processes database changes as they occur, enabling near real-time analytics and operational insights. Delta Lake’s ACID transaction … Read more
