Vincle to Mode Integration
AI-first ETL from Vincle into Mode. Governed entities, incremental sync, typed landing tables.
How Datrise loads Vincle into Mode
Datrise syncs Vincle's customers, visits, orders, product assortments, promotions, and sales routes 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
Vincle: Field sales and trade execution for consumer goods.
Mode: Collaborative analytics workspace for SQL, Python, and shared reports.
How Vincle entities map to Mode
| Vincle entity | Mode object | Notes |
|---|---|---|
| customers | vincle_customers | id PK · custom fields → flattened columns for SQL and notebooks |
| visits | vincle_visits | id PK · linked to vincle_customers |
| orders | vincle_orders | id PK · linked to vincle_customers |
| product assortments | vincle_product_assortments | id PK · linked to vincle_customers |
FAQ
How does Datrise handle Vincle'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 Vincle to Mode sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the queried tables.
Related pipelines
More destinations for Vincle
Early access
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