DatriseAI-first ETL

Streak to PostgreSQL Integration

AI-first ETL from Streak into PostgreSQL. Governed entities, incremental sync, typed landing tables.

How Datrise loads Streak into PostgreSQL

Datrise syncs Streak's pipelines, boxes, stages, email threads, contacts, and tasks into PostgreSQL as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

Streak: CRM that lives inside Gmail as pipelines and boxes.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How Streak entities map to PostgreSQL

Streak entityPostgreSQL objectNotes
pipelinesstreak_pipelinesid PK · custom fields → jsonb columns
boxesstreak_boxesid PK · linked to streak_pipelines
stagesstreak_stagesid PK · linked to streak_pipelines
email threadsstreak_email_threadsid PK · linked to streak_pipelines

FAQ

How does Datrise handle Streak's custom fields in PostgreSQL?

Flexible values are stored as jsonb columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native PostgreSQL types.

How does the Streak to PostgreSQL sync stay up to date?

It runs incrementally — Datrise uses a watermark on each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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