DatriseAI-first ETL

Azure Blob Storage to Mode Integration

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

How Datrise loads Azure Blob Storage into Mode

Datrise syncs Azure Blob Storage's containers, blobs, blob metadata, snapshots, access tiers, and blob versions into Mode as warehouse tables Mode queries with SQL. Flexible or custom fields land in flattened columns for SQL and notebooks, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the queried tables, so re-runs update only what changed. Date-partitioned facts for report queries. Mode runs analyst-written SQL, so Datrise lands stable, documented tables that won't break saved reports.

Ideal for SQL-first analysis with Python and R notebooks.

Endpoints

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

Mode: Collaborative analytics workspace for SQL, Python, and shared reports.

How Azure Blob Storage entities map to Mode

Azure Blob Storage entityMode objectNotes
containersazureblobstorage_containersid PK · custom fields → flattened columns for SQL and notebooks
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 Mode?

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

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

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

Related pipelines

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