ServiceTitan to Azure Data Lake Storage Integration
AI-first ETL from ServiceTitan into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads ServiceTitan into Azure Data Lake Storage
Datrise syncs ServiceTitan's jobs, customers, estimates, invoices, technicians, and service agreements into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
ServiceTitan: Trades platform for dispatch, jobs, and revenue reporting.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How ServiceTitan entities map to Azure Data Lake Storage
| ServiceTitan entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| jobs | servicetitan_jobs | id PK · custom fields → nested struct/map fields in Parquet |
| customers | servicetitan_customers | id PK · linked to servicetitan_jobs |
| estimates | servicetitan_estimates | id PK · linked to servicetitan_jobs |
| invoices | servicetitan_invoices | id PK · linked to servicetitan_jobs |
FAQ
How does Datrise handle ServiceTitan's custom fields in Azure Data Lake Storage?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.
How does the ServiceTitan to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for ServiceTitan
- ServiceTitan → Azure Synapse
- ServiceTitan → Spreadsheets
- ServiceTitan → Airtable
- ServiceTitan → CSV Files
- ServiceTitan → MongoDB
- ServiceTitan → Supabase
- ServiceTitan → Neon
- ServiceTitan → PlanetScale
- ServiceTitan → Amazon DynamoDB
- ServiceTitan → Looker
- ServiceTitan → Looker Studio
- ServiceTitan → Microsoft Power BI
More sources for Azure Data Lake Storage
- Housecall Pro → Azure Data Lake Storage
- Jobber → Azure Data Lake Storage
- Mindbody → Azure Data Lake Storage
- Clio → Azure Data Lake Storage
- MyCase → Azure Data Lake Storage
- Allbound → Azure Data Lake Storage
- Impartner → Azure Data Lake Storage
- ChurnZero → Azure Data Lake Storage
- Totango → Azure Data Lake Storage
- Zoho Desk → Azure Data Lake Storage
- Mautic → Azure Data Lake Storage
- Dolibarr → Azure Data Lake Storage
Early access
Connect ServiceTitan to Azure Data Lake Storage the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.