Postmark to Looker Studio Integration
AI-first ETL from Postmark into Looker Studio. Governed entities, incremental sync, typed landing tables.
How Datrise loads Postmark into Looker Studio
Datrise syncs Postmark's messages, servers, delivery events, bounces, opens, and message streams 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
Postmark: Transactional email delivery with detailed events.
Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).
How Postmark entities map to Looker Studio
| Postmark entity | Looker Studio object | Notes |
|---|---|---|
| messages | postmark_app_messages | id PK · custom fields → flattened columns for chart fields |
| servers | postmark_app_servers | id PK · linked to postmark_app_messages |
| delivery events | postmark_app_delivery_events | date dimension columns events |
| bounces | postmark_app_bounces | id PK · linked to postmark_app_messages |
FAQ
How does Datrise handle Postmark'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 Postmark to Looker Studio sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
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Early access
Connect Postmark to Looker Studio the easy way
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