DatriseAI-first ETL

Azure Table Storage Tableau

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

How Datrise loads Azure Table Storage into Tableau

Datrise syncs Azure Table Storage's records, events, and configuration objects into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.

Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.

Ideal for visual analytics and dashboards in Tableau.

Endpoints

Azure Table Storage: SaaS or API data source for analytics and warehouse sync.

Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.

How Azure Table Storage entities map to Tableau

Azure Table Storage entityTableau objectNotes
recordsazure_table_storage_recordsid PK · custom fields → flattened columns for Tableau fields
eventsazure_table_storage_eventsdate/datetime fields 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 Tableau?

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

How does the Azure Table Storage to Tableau sync stay up to date?

It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.

Related pipelines

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