DatriseAI-first ETL

Vincle to Neon Integration

AI-first ETL from Vincle into Neon. Governed entities, incremental sync, typed landing tables.

How Datrise loads Vincle into Neon

Datrise syncs Vincle's customers, visits, orders, product assortments, promotions, and sales routes into Neon as a typed table per source entity. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.

Ideal for serverless Postgres workloads that scale to zero between syncs.

Endpoints

Vincle: Field sales and trade execution for consumer goods.

Neon: Serverless Postgres destination with branching and autoscaling.

How Vincle entities map to Neon

Vincle entityNeon objectNotes
customersvincle_customersid PK · custom fields → jsonb columns
visitsvincle_visitsid PK · linked to vincle_customers
ordersvincle_ordersid PK · linked to vincle_customers
product assortmentsvincle_product_assortmentsid PK · linked to vincle_customers

FAQ

How does Datrise handle Vincle's custom fields in Neon?

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

How does the Vincle to Neon sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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