Megaplan → Mode
AI-first ETL from Megaplan into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Megaplan into Mode
Datrise syncs Megaplan's contacts, accounts, deals, activities, and lifecycle events into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.
Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.
Ideal for SQL-first analysis with Python and R notebooks.
Endpoints
Megaplan: CRM with strong adoption in CIS markets for sales and operations.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Megaplan entities map to Mode
| Megaplan entity | Mode object | Notes |
|---|---|---|
| contacts | megaplan_contacts | id PK · custom fields → flattened columns for SQL and notebooks |
| accounts | megaplan_accounts | id PK · linked to megaplan_contacts |
| deals | megaplan_deals | id PK · linked to megaplan_contacts |
| activities | megaplan_activities | temporal columns events |
FAQ
How does Datrise handle Megaplan's custom fields in Mode?
Flexible values are stored as flattened columns for SQL and notebooks, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Mode types.
How does the Megaplan to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Megaplan
Early access
Connect Megaplan to Mode the easy way
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