Mailshake to GoodData Integration
AI-first ETL from Mailshake into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mailshake into GoodData
Datrise syncs Mailshake's campaigns, recipients, sequences, email events, replies, and leads 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
Mailshake: Cold outreach sequences and reply tracking.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Mailshake entities map to GoodData
| Mailshake entity | GoodData object | Notes |
|---|---|---|
| campaigns | mailshake_campaigns | id PK · custom fields → flattened columns |
| recipients | mailshake_recipients | id PK · linked to mailshake_campaigns |
| sequences | mailshake_sequences | id PK · linked to mailshake_campaigns |
| email events | mailshake_email_events | date dimensions events |
FAQ
How does Datrise handle Mailshake'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 Mailshake to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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