DatriseAI-first ETL

Microsoft Azure to DuckDB Integration

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

How Datrise loads Microsoft Azure into DuckDB

Datrise syncs Microsoft Azure's subscriptions, resource groups, resources, usage records, costs, and activity logs into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

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

DuckDB: In-process analytics database for fast local OLAP.

How Microsoft Azure entities map to DuckDB

Microsoft Azure entityDuckDB objectNotes
subscriptionsmicrosoft_azure_subscriptionsid PK · custom fields → JSON or STRUCT columns
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 DuckDB?

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

How does the Microsoft Azure to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

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