Mindbody → GoodData
AI-first ETL from Mindbody into GoodData. Governed entities, incremental sync, typed landing tables.
How Datrise loads Mindbody into GoodData
Datrise syncs Mindbody'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
Mindbody: Wellness and fitness CRM for members, bookings, and retention.
GoodData: Composable analytics platform with headless BI and embedded dashboards.
How Mindbody entities map to GoodData
| Mindbody entity | GoodData object | Notes |
|---|---|---|
| contacts | mindbody_contacts | id PK · custom fields → flattened columns |
| accounts | mindbody_accounts | id PK · linked to mindbody_contacts |
| deals | mindbody_deals | id PK · linked to mindbody_contacts |
| activities | mindbody_activities | date dimensions events |
FAQ
How does Datrise handle Mindbody'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 Mindbody to GoodData sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the connected tables.
Related pipelines
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