Low-Latency Ingestion with Datastream
Creation of a near real-time data replication pipeline with a 98% cost reduction.
Context
Development of a Change Data Capture (CDC) data ingestion architecture to replicate a transactional database (MySQL) to an analytical environment (BigQuery) reliably, quickly, and cost-effectively.
The Problem
The previous methodology consumed significant resources from the transactional database, resulting in high processing costs.
Data loss during replication compromised information reliability.
The replication process was too slow for large tables, making near real-time analysis impossible.
The Solution & My Contribution
- Architected and implemented a native Google Cloud solution using Datastream, Cloud Storage, Composer (Airflow), and BigQuery.
- Set up CDC binlog capture to export Avro files to Cloud Storage.
- Designed a layered architecture — External Tables, Streaming Views, and Raw Tables — enabling both real-time and batch-optimized data access.
Diagrama de alto nível da arquitetura da solução do Low-Latency Ingestion with Datastream
Results & Impact
- 98% reduction in data replication costs.
- Data available for analysis within minutes (near real-time) instead of hours.
- Modular, scalable, and easily replicable ingestion architecture for multiple SaaS clients.
Technologies Used
Technical Deep Dive (Illustrative Code)
External Tables and Views Architecture
Datastream writes Avro files directly to Cloud Storage. BigQuery references these files through External Tables (covering D-2 to current day), with a View that extracts the latest records. This approach provides data updates every 2 minutes with zero processing cost until the actual query is executed. A merge select then loads the data into partitioned and clustered Raw Tables, optimizing cost and query performance. The entire pipeline is orchestrated via a DAG in Cloud Composer (Airflow).