DatriseAI-first ETL

PostgreSQL to Google BigQuery Integration

AI-first ETL from PostgreSQL into Google BigQuery. Governed entities, incremental sync, typed landing tables.

How Datrise loads PostgreSQL into Google BigQuery

Datrise syncs PostgreSQL's tables, rows, schemas, indexes, sequences, and replication slots into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

PostgreSQL: Self-managed PostgreSQL instances.

Google BigQuery: Serverless analytics warehouse on GCP.

How PostgreSQL entities map to Google BigQuery

PostgreSQL entityGoogle BigQuery objectNotes
tableshp_postgres_tablesid PK · custom fields → JSON or nested/repeated (STRUCT) columns
rowshp_postgres_rowsid PK · linked to hp_postgres_tables
schemashp_postgres_schemasid PK · linked to hp_postgres_tables
indexeshp_postgres_indexesid PK · linked to hp_postgres_tables

FAQ

How does Datrise handle PostgreSQL's custom fields in Google BigQuery?

Flexible values are stored as JSON or nested/repeated (STRUCT) columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Google BigQuery types.

How does the PostgreSQL to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

Early access

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