Amazon RDS to Looker Studio Integration

How Datrise loads Amazon RDS into Looker Studio

Datrise syncs Amazon RDS's tables, rows, schemas, indexes, primary keys, and change streams 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

Amazon RDS: Managed relational database service on AWS.

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

How Amazon RDS entities map to Looker Studio

Amazon RDS entity	Looker Studio object	Notes
tables	`amazon_rds_tables`	id PK · custom fields → flattened columns for chart fields
rows	`amazon_rds_rows`	id PK · linked to amazon_rds_tables
schemas	`amazon_rds_schemas`	id PK · linked to amazon_rds_tables
indexes	`amazon_rds_indexes`	id PK · linked to amazon_rds_tables

FAQ

How does Datrise handle Amazon RDS'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 Amazon RDS 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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