Google Analytics → Sisense
AI-first ETL from Google Analytics into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Analytics into Sisense
Datrise syncs Google Analytics's sessions, events, channels, conversions, and behavior cohorts 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 Analytics: Web and product analytics for behavior and traffic insights.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Google Analytics entities map to Sisense
| Google Analytics entity | Sisense object | Notes |
|---|---|---|
| sessions | google_analytics_sessions | id PK · custom fields → flattened columns for the cube |
| events | google_analytics_events | date/time fields events |
| channels | google_analytics_channels | id PK · linked to google_analytics_sessions |
| conversions | google_analytics_conversions | id PK · linked to google_analytics_sessions |
FAQ
How does Datrise handle Google Analytics'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 Analytics to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Google Analytics
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- Google Analytics → Yellowfin
Early access
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