DatriseAI-first ETL

Close Amazon Athena

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

How Datrise loads Close into Amazon Athena

Datrise syncs Close's leads, opportunities, calls, SMS events, and sequence performance 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

Close: Inside-sales CRM with calling and sequences.

Amazon Athena: Serverless SQL over S3 data lake tables.

How Close entities map to Amazon Athena

Close entityAmazon Athena objectNotes
leadsclose_leadsid PK · custom fields → struct/map columns in Parquet
opportunitiesclose_opportunitiesid PK · linked to close_leads
callsclose_callsid PK · linked to close_leads
SMS eventsclose_sms_eventstimestamp events

FAQ

How does Datrise handle Close'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 Close 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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