Bridging the gap between analytical systems (OLAP) and transactional databases (OLTP) is notoriously complex. Synchronizing data between the two systems, often called Reverse ETL (Extract, Transform, Load), traditionally requires heavy middleware, staging buckets, and complex orchestration, especially at enterprise scale.
We’re announcing the general availability (GA) of BigQuery exports to AlloyDB, allowing data engineering teams to use a single, declarative SQL statement to export data from BigQuery to AlloyDB. This approach completely removes the need for complex ETL middleware.
To demonstrate the benefits of this feature, we are sharing how Copel, a Brazilian electric power provider, eliminated a massive orchestration bottleneck and scaled their smart meter telemetry by migrating to high-performance, built-in Reverse ETL.
The challenge of scaling smart meter telemetry
At Copel, managing telemetry data from millions of smart electricity meters is a mission-critical operation. This data is streamed continuously and consolidated in an analytical Gold layer in BigQuery. However, to power Copel’s “Systems of Action” — low-latency customer support dashboards, mobile applications, and operational billing systems — this rich dataset must be constantly synced back into its high-performance transactional database, AlloyDB for PostgreSQL.
Until recently, Copel orchestrated this Reverse ETL architecture with a complex DAG in Google’s Managed Service for Apache Airflow. However, there were multiple brittle, intermediate stages in the process of enriching and exporting hundreds of millions of records from BigQuery to AlloyDB:
Source Credit: https://cloud.google.com/blog/products/data-analytics/reverse-etl-bigquery-exports-to-alloydb/
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