DatriseAI-first ETL

Salesforce Amazon DynamoDB

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

How Datrise loads Salesforce into Amazon DynamoDB

Datrise syncs Salesforce's accounts, opportunities, contacts, tasks, and pipeline stage history 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

Salesforce: Enterprise CRM and Customer 360 platform.

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

How Salesforce entities map to Amazon DynamoDB

Salesforce entityAmazon DynamoDB objectNotes
accountssalesforce_accountsid PK · custom fields → nested map/list attributes
opportunitiessalesforce_opportunitiesid PK · linked to salesforce_accounts
contactssalesforce_contactsid PK · linked to salesforce_accounts
taskssalesforce_tasksid PK · linked to salesforce_accounts

FAQ

How does Datrise handle Salesforce'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 Salesforce 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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