DatriseAI-first ETL

Google Cloud SQL to GoodData Integration

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 tables, rows, schemas, indexes, read replicas, and binlog changes 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: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.

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

How Google Cloud SQL entities map to GoodData

Google Cloud SQL entityGoodData objectNotes
tablesgoogle_cloud_sql_tablesid PK · custom fields → flattened columns
rowsgoogle_cloud_sql_rowsid PK · linked to google_cloud_sql_tables
schemasgoogle_cloud_sql_schemasid PK · linked to google_cloud_sql_tables
indexesgoogle_cloud_sql_indexesid PK · linked to google_cloud_sql_tables

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

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.