DatriseAI-first ETL

Clay Amazon DynamoDB

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

How Datrise loads Clay into Amazon DynamoDB

Datrise syncs Clay's contacts, accounts, deals, activities, and lifecycle events 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

Clay: AI-native CRM for relationship data, enrichment, and workflow automation.

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

How Clay entities map to Amazon DynamoDB

Clay entityAmazon DynamoDB objectNotes
contactsclay_contactsid PK · custom fields → nested map/list attributes
accountsclay_accountsid PK · linked to clay_contacts
dealsclay_dealsid PK · linked to clay_contacts
activitiesclay_activitiesISO-8601 string or epoch number attributes events

FAQ

How does Datrise handle Clay'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 Clay 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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