DatriseAI-first ETL

Google Cloud SQL GoodData

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

How Datrise loads Google Cloud SQL into GoodData

Datrise syncs Google Cloud SQL'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: SaaS or API data source for analytics and warehouse sync.

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

How Google Cloud SQL entities map to GoodData

Google Cloud SQL entityGoodData objectNotes
recordsgoogle_cloud_sql_recordsid PK · custom fields → flattened columns
eventsgoogle_cloud_sql_eventsdate dimensions events
configuration objectsgoogle_cloud_sql_configuration_objectsid PK · linked to google_cloud_sql_records

FAQ

How does Datrise handle Google Cloud SQL'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 to GoodData sync stay up to date?

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

Related pipelines

Early access

Connect Google Cloud SQL to GoodData the easy way

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