Megaplan → Tableau
AI-first ETL from Megaplan into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Megaplan into Tableau
Datrise syncs Megaplan's contacts, accounts, deals, activities, and lifecycle events into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
Megaplan: CRM with strong adoption in CIS markets for sales and operations.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Megaplan entities map to Tableau
| Megaplan entity | Tableau object | Notes |
|---|---|---|
| contacts | megaplan_contacts | id PK · custom fields → flattened columns for Tableau fields |
| accounts | megaplan_accounts | id PK · linked to megaplan_contacts |
| deals | megaplan_deals | id PK · linked to megaplan_contacts |
| activities | megaplan_activities | date/datetime fields events |
FAQ
How does Datrise handle Megaplan's custom fields in Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the Megaplan to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
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