DatriseAI-first ETL

Megaplan to Sisense Integration

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

How Datrise loads Megaplan into Sisense

Datrise syncs Megaplan's deals, contractors, contacts, tasks, projects, and invoices 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

Megaplan: CRM and task management for CIS sales and service teams.

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

How Megaplan entities map to Sisense

Megaplan entitySisense objectNotes
dealsmegaplan_dealsid PK · custom fields → flattened columns for the cube
contractorsmegaplan_contractorsid PK · linked to megaplan_deals
contactsmegaplan_contactsid PK · linked to megaplan_deals
tasksmegaplan_tasksid PK · linked to megaplan_deals

FAQ

How does Datrise handle Megaplan'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 Megaplan to Sisense sync stay up to date?

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

Related pipelines

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