DatriseAI-first ETL

Looker to Amazon DynamoDB Integration

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

How Datrise loads Looker into Amazon DynamoDB

Datrise syncs Looker's looks, dashboards, explores, queries, users, and scheduled plans into Amazon DynamoDB as an item per source record in a table per entity. Flexible or custom fields land in nested map/list attributes, and timestamps such as created, updated, and status changes are typed as ISO-8601 string or epoch number attributes.

Sync is incremental: Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id, so re-runs update only what changed. Partition-key design on the entity id to spread throughput evenly. DynamoDB rewards access-pattern-first key design, so Datrise sets partition/sort keys from your entity ids rather than scan-heavy defaults.

Ideal for serverless apps needing single-digit-millisecond key lookups on CRM data.

Endpoints

Looker: BI platform with a modeled semantic layer.

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

How Looker entities map to Amazon DynamoDB

Looker entityAmazon DynamoDB objectNotes
lookslooker_looksid PK · custom fields → nested map/list attributes
dashboardslooker_dashboardsid PK · linked to looker_looks
exploreslooker_exploresid PK · linked to looker_looks
querieslooker_queriesid PK · linked to looker_looks

FAQ

How does Datrise handle Looker's custom fields in Amazon DynamoDB?

Flexible values are stored as nested map/list attributes, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon DynamoDB types.

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

It runs incrementally — Datrise uses PutItem/UpdateItem keyed on a partition key derived from the entity id.

Related pipelines

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