DatriseAI-first ETL

Salesforce Pardot Amazon Athena

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

How Datrise loads Salesforce Pardot into Amazon Athena

Datrise syncs Salesforce Pardot's contacts, accounts, deals, activities, and lifecycle events 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 Pardot: Marketing automation platform with CRM and lifecycle engagement.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Salesforce Pardot entities map to Amazon Athena

Salesforce Pardot entityAmazon Athena objectNotes
contactssalesforce_pardot_contactsid PK · custom fields → struct/map columns in Parquet
accountssalesforce_pardot_accountsid PK · linked to salesforce_pardot_contacts
dealssalesforce_pardot_dealsid PK · linked to salesforce_pardot_contacts
activitiessalesforce_pardot_activitiestimestamp events

FAQ

How does Datrise handle Salesforce Pardot'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 Pardot 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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