Streak to Mode Integration
AI-first ETL from Streak into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Streak into Mode
Datrise syncs Streak's pipelines, boxes, stages, email threads, contacts, and tasks into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Streak: CRM that lives inside Gmail as pipelines and boxes.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Streak entities map to Mode
| Streak entity | Mode object | Notes |
|---|---|---|
| pipelines | streak_pipelines | id PK · custom fields → flattened columns for SQL and notebooks |
| 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 Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Streak to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Streak
Early access
Connect Streak to Mode the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.