DatriseAI-first ETL

Azure Blob Storage to Google BigQuery Integration

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

How Datrise loads Azure Blob Storage into Google BigQuery

Datrise syncs Azure Blob Storage's containers, blobs, blob metadata, snapshots, access tiers, and blob versions into Google BigQuery as a partitioned table per source entity. Flexible or custom fields land in JSON or nested/repeated (STRUCT) columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target, so re-runs update only what changed. Partition by ingestion or event date and cluster by entity id to keep scanned bytes low. BigQuery bills by bytes scanned, so Datrise partitions and clusters every table to keep query costs predictable.

Ideal for Google-stack analytics and ML on serverless infrastructure.

Endpoints

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

Google BigQuery: Serverless analytics warehouse on GCP.

How Azure Blob Storage entities map to Google BigQuery

Azure Blob Storage entityGoogle BigQuery objectNotes
containersazureblobstorage_containersid PK · custom fields → JSON or nested/repeated (STRUCT) columns
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 Google BigQuery?

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

How does the Azure Blob Storage to Google BigQuery sync stay up to date?

It runs incrementally — Datrise uses appends to a staging table, then MERGE on stable id into the partitioned target.

Related pipelines

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