DatriseAI-first ETL

Google Cloud SQL Sisense

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

How Datrise loads Google Cloud SQL into Sisense

Datrise syncs Google Cloud SQL's records, events, and configuration objects into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

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

Sisense: Analytics platform with elastic data models and embedded analytics.

How Google Cloud SQL entities map to Sisense

Google Cloud SQL entitySisense objectNotes
recordsgoogle_cloud_sql_recordsid PK · custom fields → flattened columns for the cube
eventsgoogle_cloud_sql_eventsdate/time fields 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 Sisense?

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

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

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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