Google BigQuery to Sisense Integration

How Datrise loads Google BigQuery into Sisense

Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions 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 BigQuery: Serverless analytics warehouse on Google Cloud.

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

How Google BigQuery entities map to Sisense

Google BigQuery entity	Sisense object	Notes
datasets	`bigquery_datasets`	id PK · custom fields → flattened columns for the cube
tables	`bigquery_tables`	id PK · linked to bigquery_datasets
views	`bigquery_views`	id PK · linked to bigquery_datasets
query jobs	`bigquery_query_jobs`	id PK · linked to bigquery_datasets

FAQ

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

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

Related pipelines

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