DatriseAI-first ETL

Azure Table Storage to Spotfire Integration

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

How Datrise loads Azure Table Storage into Spotfire

Datrise syncs Azure Table Storage's tables, entities, partition keys, row keys, properties, and timestamps into Spotfire as warehouse tables or in-memory data for Spotfire analyses. Flexible or custom fields land in flattened columns for visualizations, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables or in-memory data, so re-runs update only what changed. Date-partitioned facts. Spotfire can load data in-memory, so Datrise keeps the backing tables incremental so analyses refresh without full reloads.

Ideal for interactive analytical visualization and data science.

Endpoints

Azure Table Storage: Key-value NoSQL store on Azure.

Spotfire: Visual analytics platform for interactive dashboards and data science workflows.

How Azure Table Storage entities map to Spotfire

Azure Table Storage entitySpotfire objectNotes
tablesazure_table_storage_tablesid PK · custom fields → flattened columns for visualizations
entitiesazure_table_storage_entitiesid PK · linked to azure_table_storage_tables
partition keysazure_table_storage_partition_keysid PK · linked to azure_table_storage_tables
row keysazure_table_storage_row_keysid PK · linked to azure_table_storage_tables

FAQ

How does Datrise handle Azure Table Storage's custom fields in Spotfire?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables or in-memory data.

Related pipelines

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