Low-Latency Ingestion with Datastream

Context

Developed a Change Data Capture (CDC) data ingestion architecture to replicate data from a transactional database (MySQL) to an analytical environment (BigQuery) in a fast, reliable, and cost-effective manner. The solution was designed to minimize latency and operational costs while offering a robust and scalable approach.

Problem

The previous methodology consumed significant resources from the transactional database, driving up processing costs.

Data loss during replication compromised information integrity and reliability.

Replication time was too slow for large tables, making real-time data analysis unfeasible.

Solution and Contribution

• Architected and implemented a native Google Cloud solution using Datastream, Cloud Storage, Composer (Airflow), and BigQuery.
• Set up CDC binlog capture to export data in Avro format to Cloud Storage.
• Designed a layered architecture — External Tables, Streaming Views, and Raw Tables — ensuring real-time access and optimized batch analysis.

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Diagrama de alto nível da arquitetura da solução do Low-Latency Ingestion with Datastream

Results and Impacts

• 98% reduction in data replication costs.
• Data available for analysis within minutes (near real-time) instead of hours.
• Creation of a modular, scalable, and easily replicable ingestion architecture for multiple SaaS clients.

Technologies Used

Technical Details