DatriseAI-first ETL

Azure Blob Storage to Looker Studio Integration

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

How Datrise loads Azure Blob Storage into Looker Studio

Datrise syncs Azure Blob Storage's containers, blobs, blob metadata, snapshots, access tiers, and blob versions 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

Azure Blob Storage: Object storage for blobs and data lake files on Azure.

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

How Azure Blob Storage entities map to Looker Studio

Azure Blob Storage entityLooker Studio objectNotes
containersazureblobstorage_containersid PK · custom fields → flattened columns for chart fields
blobsazureblobstorage_blobsid PK · linked to azureblobstorage_containers
blob metadataazureblobstorage_blob_metadataid PK · linked to azureblobstorage_containers
snapshotsazureblobstorage_snapshotsid PK · linked to azureblobstorage_containers

FAQ

How does Datrise handle Azure Blob Storage'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 Azure Blob Storage 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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