DatriseAI-first ETL

Amazon DynamoDB to Spotfire Integration

AI-first ETL from Amazon DynamoDB into Spotfire. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon DynamoDB into Spotfire

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records 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

Amazon DynamoDB: Serverless key-value and document store on AWS.

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

How Amazon DynamoDB entities map to Spotfire

Amazon DynamoDB entitySpotfire objectNotes
tablessource_dynamodb_tablesid PK · custom fields → flattened columns for visualizations
itemssource_dynamodb_itemsid PK · linked to source_dynamodb_tables
partition keyssource_dynamodb_partition_keysid PK · linked to source_dynamodb_tables
sort keyssource_dynamodb_sort_keysid PK · linked to source_dynamodb_tables

FAQ

How does Datrise handle Amazon DynamoDB'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 Amazon DynamoDB 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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