DatriseAI-first ETL

Dixa Neon

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

How Datrise loads Dixa into Neon

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics 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

Dixa: Customer service platform for conversations across channels.

Neon: Serverless Postgres destination with branching and autoscaling.

How Dixa entities map to Neon

Dixa entityNeon objectNotes
conversationsdixa_conversationsid PK · custom fields → jsonb columns
agentsdixa_agentsid PK · linked to dixa_conversations
customersdixa_customersid PK · linked to dixa_conversations
tagsdixa_tagsid PK · linked to dixa_conversations

FAQ

How does Datrise handle Dixa'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 Dixa 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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