DatriseAI-first ETL

Dixa Looker Studio

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

How Datrise loads Dixa into Looker Studio

Datrise syncs Dixa's conversations, agents, customers, tags, and resolution metrics into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

Dixa: Customer service platform for conversations across channels.

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Dixa entities map to Looker Studio

Dixa entityLooker Studio objectNotes
conversationsdixa_conversationsid PK · custom fields → flattened columns for chart fields
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 Looker Studio?

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

How does the Dixa to Looker Studio sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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