DatriseAI-first ETL

Freshsales Amazon Athena

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

How Datrise loads Freshsales into Amazon Athena

Datrise syncs Freshsales's leads, contacts, deals, calls, and email activity telemetry 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

Freshsales: CRM by Freshworks with built-in phone and email.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Freshsales entities map to Amazon Athena

Freshsales entityAmazon Athena objectNotes
leadsfreshsales_leadsid PK · custom fields → struct/map columns in Parquet
contactsfreshsales_contactsid PK · linked to freshsales_leads
dealsfreshsales_dealsid PK · linked to freshsales_leads
callsfreshsales_callsid PK · linked to freshsales_leads

FAQ

How does Datrise handle Freshsales'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 Freshsales 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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