DatriseAI-first ETL

PostgreSQL to Looker Studio Integration

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

How Datrise loads PostgreSQL into Looker Studio

Datrise syncs PostgreSQL's tables, rows, schemas, indexes, sequences, and replication slots into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

PostgreSQL: Self-managed PostgreSQL instances.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How PostgreSQL entities map to Looker Studio

PostgreSQL entityLooker Studio objectNotes
tableshp_postgres_tablesid PK · custom fields → flattened columns for chart fields
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 Looker Studio?

Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.

How does the PostgreSQL to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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