DatriseAI-first ETL

Salesforce Amazon Athena

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

How Datrise loads Salesforce into Amazon Athena

Datrise syncs Salesforce's accounts, opportunities, contacts, tasks, and pipeline stage history into Amazon Athena as partitioned Parquet in S3 exposed as an Athena table. Flexible or custom fields land in struct/map columns in Parquet, and timestamps such as created, updated, and status changes are typed as timestamp.

Sync is incremental: Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog, so re-runs update only what changed. Hive-style partitioning by load date so Athena scans only new data. Athena bills per byte scanned and small files hurt, so Datrise compacts to right-sized Parquet rather than many tiny objects.

Ideal for serverless SQL over an S3 lake without a running warehouse.

Endpoints

Salesforce: Enterprise CRM and Customer 360 platform.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Salesforce entities map to Amazon Athena

Salesforce entityAmazon Athena objectNotes
accountssalesforce_accountsid PK · custom fields → struct/map columns in Parquet
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 Athena?

Flexible values are stored as struct/map columns in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon Athena types.

How does the Salesforce to Amazon Athena sync stay up to date?

It runs incrementally — Datrise uses writes new Parquet partitions and registers them in the Glue Data Catalog.

Related pipelines

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