DatriseAI-first ETL

Dixa PostgreSQL

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

How Datrise loads Dixa into PostgreSQL

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics into PostgreSQL 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 each entity's updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative range partitioning by load date for high-volume tables. PostgreSQL folds unquoted identifiers to lowercase, so Datrise normalizes mixed-case source fields to snake_case.

Ideal for operational analytics and application backends that need fresh, queryable copies of your data.

Endpoints

Dixa: Customer service platform for conversations across channels.

PostgreSQL: Open-source relational database with strong SQL and extensions.

How Dixa entities map to PostgreSQL

Dixa entityPostgreSQL 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 PostgreSQL?

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 PostgreSQL types.

How does the Dixa to PostgreSQL sync stay up to date?

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

Related pipelines

Early access

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