DatriseAI-first ETL

ServiceTitan to Snowflake Integration

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

How Datrise loads ServiceTitan into Snowflake

Datrise syncs ServiceTitan's jobs, customers, estimates, invoices, technicians, and service agreements into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

ServiceTitan: Trades platform for dispatch, jobs, and revenue reporting.

Snowflake: Cloud data warehouse with separated compute and storage.

How ServiceTitan entities map to Snowflake

ServiceTitan entitySnowflake objectNotes
jobsservicetitan_jobsid PK · custom fields → VARIANT columns
customersservicetitan_customersid PK · linked to servicetitan_jobs
estimatesservicetitan_estimatesid PK · linked to servicetitan_jobs
invoicesservicetitan_invoicesid PK · linked to servicetitan_jobs

FAQ

How does Datrise handle ServiceTitan's custom fields in Snowflake?

Flexible values are stored as VARIANT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Snowflake types.

How does the ServiceTitan to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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