DatriseAI-first ETL

ServiceTitan Amazon Athena

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

How Datrise loads ServiceTitan into Amazon Athena

Datrise syncs ServiceTitan'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

ServiceTitan: Field service CRM for scheduling, jobs, and customer history.

Amazon Athena: Serverless SQL over S3 data lake tables.

How ServiceTitan entities map to Amazon Athena

ServiceTitan entityAmazon Athena objectNotes
contactsservicetitan_contactsid PK · custom fields → struct/map columns in Parquet
accountsservicetitan_accountsid PK · linked to servicetitan_contacts
dealsservicetitan_dealsid PK · linked to servicetitan_contacts
activitiesservicetitan_activitiestimestamp events

FAQ

How does Datrise handle ServiceTitan'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 ServiceTitan 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

Early access

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