Megaplan to DuckDB Integration

How Datrise loads Megaplan into DuckDB

Datrise syncs Megaplan's deals, contractors, contacts, tasks, projects, and invoices into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

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

DuckDB: In-process analytics database for fast local OLAP.

How Megaplan entities map to DuckDB

Megaplan entity	DuckDB object	Notes
deals	`megaplan_deals`	id PK · custom fields → JSON or STRUCT columns
contractors	`megaplan_contractors`	id PK · linked to megaplan_deals
contacts	`megaplan_contacts`	id PK · linked to megaplan_deals
tasks	`megaplan_tasks`	id PK · linked to megaplan_deals

FAQ

How does Datrise handle Megaplan's custom fields in DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the Megaplan to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

Connect Megaplan to DuckDB the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.