DatriseAI-first ETL

Amazon DynamoDB to Looker Studio Integration

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

How Datrise loads Amazon DynamoDB into Looker Studio

Datrise syncs Amazon DynamoDB's tables, items, partition keys, sort keys, attributes, and stream records into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

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

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Amazon DynamoDB entities map to Looker Studio

Amazon DynamoDB entityLooker Studio objectNotes
tablessource_dynamodb_tablesid PK · custom fields → flattened columns for chart 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 Looker Studio?

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

How does the Amazon DynamoDB to Looker Studio sync stay up to date?

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

Related pipelines

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