DatriseAI-first ETL

Mindbody to Sisense Integration

AI-first ETL from Mindbody into Sisense. Governed entities, incremental sync, typed landing tables.

How Datrise loads Mindbody into Sisense

Datrise syncs Mindbody's clients, class bookings, memberships, staff, sales, and appointments into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.

Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.

Ideal for embedded analytics on an in-memory engine.

Endpoints

Mindbody: Booking and membership platform for wellness studios.

Sisense: Analytics platform with elastic data models and embedded analytics.

How Mindbody entities map to Sisense

Mindbody entitySisense objectNotes
clientsmindbody_clientsid PK · custom fields → flattened columns for the cube
class bookingsmindbody_class_bookingsid PK · linked to mindbody_clients
membershipsmindbody_membershipsid PK · linked to mindbody_clients
staffmindbody_staffid PK · linked to mindbody_clients

FAQ

How does Datrise handle Mindbody's custom fields in Sisense?

Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.

How does the Mindbody to Sisense sync stay up to date?

It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.

Related pipelines

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