The first Postgres to BigQuery pipeline most teams write is a nightly export followed by bq load . It holds up until someone asks why an order that changed this morning still shows yesterday's status, or why a row deleted in Postgres is still counted in a report.

The common ways to move the data:

A scheduled export and bq load . A script, a scheduler and a staging bucket. Freshness is hours to daily, and updates and deletes need full reloads or your own merge.

Datastream for BigQuery . Google's managed change data capture (CDC) service. You set up a connection profile and a stream, run nothing yourself, and the rows land in native BigQuery datasets.

Debezium and Kafka with a BigQuery sink connector. Changes arrive within seconds. You operate Kafka, Kafka Connect, Debezium and the sink connector.

Managed extract, load, transform (ELT) tools such as Fivetran, Estuary or Airbyte Cloud. Nothing to host, and freshness in minutes depending on the plan.…