DatriseAI-first ETL

Amazon DynamoDB to ThoughtSpot Integration

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

How Datrise loads Amazon DynamoDB into ThoughtSpot

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.

Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.

Ideal for natural-language search analytics over a warehouse.

Endpoints

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

ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.

How Amazon DynamoDB entities map to ThoughtSpot

Amazon DynamoDB entityThoughtSpot objectNotes
tablessource_dynamodb_tablesid PK · custom fields → flattened columns for searchable fields
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 ThoughtSpot?

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

How does the Amazon DynamoDB to ThoughtSpot sync stay up to date?

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

Related pipelines

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