DatriseAI-first ETL

Google Cloud SQL to Spotfire Integration

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

How Datrise loads Google Cloud SQL into Spotfire

Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Google Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Google Cloud SQL entities map to Spotfire

Google Cloud SQL entitySpotfire objectNotes
tablesgoogle_cloud_sql_tablesid PK · custom fields → flattened columns for visualizations
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 Spotfire?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

Early access

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