folk → GoodData
AI-first ETL from folk into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads folk into GoodData
Datrise syncs folk's contacts, accounts, deals, activities, and lifecycle events 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
folk: AI-native CRM for relationship data, enrichment, and workflow automation.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How folk entities map to GoodData
| folk entity | GoodData object | Notes |
|---|---|---|
| contacts | folk_contacts | id PK · custom fields → flattened columns |
| accounts | folk_accounts | id PK · linked to folk_contacts |
| deals | folk_deals | id PK · linked to folk_contacts |
| activities | folk_activities | date dimensions events |
FAQ
How does Datrise handle folk'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 folk to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
More destinations for folk
Early access
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