Postmark to GoodData Integration
AI-first ETL from Postmark into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Postmark into GoodData
Datrise syncs Postmark's messages, servers, delivery events, bounces, opens, and message streams into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.
Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.
Ideal for embedded, multi-tenant analytics.
Endpoints
Postmark: Transactional email delivery with detailed events.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Postmark entities map to GoodData
| Postmark entity | GoodData object | Notes |
|---|---|---|
| messages | postmark_app_messages | id PK · custom fields → flattened columns |
| servers | postmark_app_servers | id PK · linked to postmark_app_messages |
| delivery events | postmark_app_delivery_events | date dimensions events |
| bounces | postmark_app_bounces | id PK · linked to postmark_app_messages |
FAQ
How does Datrise handle Postmark's custom fields in GoodData?
Flexible values are stored as flattened columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native GoodData types.
How does the Postmark to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for Postmark
Early access
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