Salesforce Service Cloud to Azure Data Lake Storage Integration
AI-first ETL from Salesforce Service Cloud into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Salesforce Service Cloud into Azure Data Lake Storage
Datrise syncs Salesforce Service Cloud's cases, contacts, accounts, entitlements, milestones, and case comments 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
Salesforce Service Cloud: Salesforce case management for support and field service.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Salesforce Service Cloud entities map to Azure Data Lake Storage
| Salesforce Service Cloud entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| cases | salesforce_service_cloud_cases | id PK · custom fields → nested struct/map fields in Parquet |
| contacts | salesforce_service_cloud_contacts | id PK · linked to salesforce_service_cloud_cases |
| accounts | salesforce_service_cloud_accounts | id PK · linked to salesforce_service_cloud_cases |
| entitlements | salesforce_service_cloud_entitlements | id PK · linked to salesforce_service_cloud_cases |
FAQ
How does Datrise handle Salesforce Service Cloud'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 Salesforce Service Cloud 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 Salesforce Service Cloud
- Salesforce Service Cloud → Azure Synapse
- Salesforce Service Cloud → Spreadsheets
- Salesforce Service Cloud → Airtable
- Salesforce Service Cloud → CSV Files
- Salesforce Service Cloud → MongoDB
- Salesforce Service Cloud → Supabase
- Salesforce Service Cloud → Neon
- Salesforce Service Cloud → PlanetScale
- Salesforce Service Cloud → Amazon DynamoDB
- Salesforce Service Cloud → Looker
- Salesforce Service Cloud → Looker Studio
- Salesforce Service Cloud → Microsoft Power BI
More sources for Azure Data Lake Storage
- Less Annoying CRM → Azure Data Lake Storage
- Streak → Azure Data Lake Storage
- Apptivo → Azure Data Lake Storage
- folk → Azure Data Lake Storage
- Clay → Azure Data Lake Storage
- Day.ai → Azure Data Lake Storage
- Twenty CRM → Azure Data Lake Storage
- Maximizer CRM → Azure Data Lake Storage
- Method:CRM → Azure Data Lake Storage
- EngageBay → Azure Data Lake Storage
- Megaplan → Azure Data Lake Storage
- 1С:CRM → Azure Data Lake Storage
Early access
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