DatriseAI-first ETL

ForceManager Amazon Athena

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

How Datrise loads ForceManager into Amazon Athena

Datrise syncs ForceManager'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

ForceManager: European CRM for SMB and mid-market sales teams.

Amazon Athena: Serverless SQL over S3 data lake tables.

How ForceManager entities map to Amazon Athena

ForceManager entityAmazon Athena objectNotes
contactsforcemanager_contactsid PK · custom fields → struct/map columns in Parquet
accountsforcemanager_accountsid PK · linked to forcemanager_contacts
dealsforcemanager_dealsid PK · linked to forcemanager_contacts
activitiesforcemanager_activitiestimestamp events

FAQ

How does Datrise handle ForceManager'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 ForceManager 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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