DatriseAI-first ETL

Azure Table Storage Mode

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

How Datrise loads Azure Table Storage into Mode

Datrise syncs Azure Table Storage's records, events, and configuration objects 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 Table Storage: SaaS or API data source for analytics and warehouse sync.

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

How Azure Table Storage entities map to Mode

Azure Table Storage entityMode objectNotes
recordsazure_table_storage_recordsid PK · custom fields → flattened columns for SQL and notebooks
eventsazure_table_storage_eventstemporal columns events
configuration objectsazure_table_storage_configuration_objectsid PK · linked to azure_table_storage_records

FAQ

How does Datrise handle Azure Table 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 Table Storage to Mode sync stay up to date?

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

Related pipelines

Early access

Connect Azure Table Storage to Mode the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.