Streak to Looker Studio Integration
AI-first ETL from Streak into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Streak into Looker Studio
Datrise syncs Streak's pipelines, boxes, stages, email threads, contacts, and tasks into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.
Ideal for free, shareable dashboards on Google data sources.
Endpoints
Streak: CRM that lives inside Gmail as pipelines and boxes.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Streak entities map to Looker Studio
| Streak entity | Looker Studio object | Notes |
|---|---|---|
| pipelines | streak_pipelines | id PK · custom fields → flattened columns for chart fields |
| boxes | streak_boxes | id PK · linked to streak_pipelines |
| stages | streak_stages | id PK · linked to streak_pipelines |
| email threads | streak_email_threads | id PK · linked to streak_pipelines |
FAQ
How does Datrise handle Streak's custom fields in Looker Studio?
Flexible values are stored as flattened columns for chart fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker Studio types.
How does the Streak to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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More sources for Looker Studio
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