DatriseAI-first ETL

Microsoft Azure to Looker Studio Integration

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

How Datrise loads Microsoft Azure into Looker Studio

Datrise syncs Microsoft Azure's subscriptions, resource groups, resources, usage records, costs, and activity logs 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

Microsoft Azure: Azure subscription resources, usage, and billing.

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

How Microsoft Azure entities map to Looker Studio

Microsoft Azure entityLooker Studio objectNotes
subscriptionsmicrosoft_azure_subscriptionsid PK · custom fields → flattened columns for chart fields
resource groupsmicrosoft_azure_resource_groupsid PK · linked to microsoft_azure_subscriptions
resourcesmicrosoft_azure_resourcesid PK · linked to microsoft_azure_subscriptions
usage recordsmicrosoft_azure_usage_recordsid PK · linked to microsoft_azure_subscriptions

FAQ

How does Datrise handle Microsoft Azure'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 Microsoft Azure 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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