DatriseAI-first ETL

Google Cloud Storage to Sisense Integration

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

How Datrise loads Google Cloud Storage into Sisense

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes 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 Storage: Object storage for files and data lake exports on GCP.

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

How Google Cloud Storage entities map to Sisense

Google Cloud Storage entitySisense objectNotes
bucketsgoogle_cloud_storage_f_bucketsid PK · custom fields → flattened columns for the cube
objectsgoogle_cloud_storage_f_objectsid PK · linked to google_cloud_storage_f_buckets
prefixesgoogle_cloud_storage_f_prefixesid PK · linked to google_cloud_storage_f_buckets
object metadatagoogle_cloud_storage_f_object_metadataid PK · linked to google_cloud_storage_f_buckets

FAQ

How does Datrise handle Google Cloud Storage'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 Storage to Sisense sync stay up to date?

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

Related pipelines

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