DatriseAI-first ETL

Google Cloud SQL Postgresql GoodData

AI-first ETL from Google Cloud SQL Postgresql into GoodData. Governed entities, incremental sync, typed landing tables.

How Datrise loads Google Cloud SQL Postgresql into GoodData

Datrise syncs Google Cloud SQL Postgresql's records, events, and configuration objects into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

Google Cloud SQL Postgresql: SaaS or API data source for analytics and warehouse sync.

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Google Cloud SQL Postgresql entities map to GoodData

Google Cloud SQL Postgresql entityGoodData objectNotes
recordsgoogle_cloud_sql_postgresql_recordsid PK · custom fields → flattened columns
eventsgoogle_cloud_sql_postgresql_eventsdate dimensions events
configuration objectsgoogle_cloud_sql_postgresql_configuration_objectsid PK · linked to google_cloud_sql_postgresql_records

FAQ

How does Datrise handle Google Cloud SQL Postgresql's custom fields in GoodData?

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

How does the Google Cloud SQL Postgresql to GoodData sync stay up to date?

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

Related pipelines

Early access

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