Data Streams are a critical component in Salesforce Data Cloud, enabling you to ingest, transform, and unify data from various sources like Salesforce, Marketing Cloud, cloud storage, and third-party APIs. They play a key role in delivering clean, real-time customer insights.
This blog post explains what Data Streams are, how they work, their architectural layers, use cases, and best practices for professionals working with Salesforce Data Cloud.
What is a Data Stream?
A Data Stream is a pipeline that pulls data from your source system into Salesforce Data Cloud and maps it to standardized Data Model Objects (DMOs). This allows Data Cloud to process, harmonize, and unify the information across all your touchpoints.
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Salesforce Data Cloud Architecture
The diagram below shows the architecture of how data flows through Salesforce Data Cloud layers, from ingestion to identity resolution.
Supported Data Stream Sources
| Source Type | Description | Example |
|---|---|---|
| Salesforce CRM | Standard & custom object data from Sales/Service Cloud | Leads, Contacts |
| Marketing Cloud | Engagement & journey data | Email sends, opens |
| Cloud Storage | CSV/JSON via Amazon S3 or Google Cloud | Event logs |
| Web & Mobile SDK | Behavioral event tracking | Page views, product clicks |
| External APIs | Third-party or custom data via MuleSoft or ETL | POS, Loyalty |
Inside the Architecture of a Data Stream
- Ingestion Layer: Pulls data from sources like Salesforce, S3, or APIs
- Transformation Layer: Maps data to DMOs using field mapping
- Data Harmonization: Standardizes values, formats, and data types
- Identity Resolution: Uses matching rules to unify identities across datasets
- Unified Profile: Final, golden record that powers segmentation and insights
Advanced Features of Data Streams
- Delta Updates: Ingest only changed records instead of full loads
- Ingestion Logs: Detailed logs for job status, record counts, and errors
- Retention & Expiry: Set automatic deletion rules for outdated data
- Preview & Validation: View samples and check mappings before going live
Real-World Use Cases
| Industry | Sources | Use Case |
|---|---|---|
| Retail | POS, Ecommerce, SFMC | Unified customer journey across store & web |
| Banking | Core banking + email engagement | Trigger personalized offers based on account activity |
| B2B SaaS | Salesforce CRM, Helpdesk, Web | Track entire lifecycle from lead to renewal |
How to Test and Validate Data Streams
- Use Preview Data to verify incoming records
- Run Field Mapping Validator to ensure schema match
- Monitor the Status Dashboard for failures and alerts
- Test Segment Updates after ingestion to confirm flow
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Troubleshooting & Common Issues
- Null primary keys? Records won’t unify—check your source mappings
- File schema mismatch? Ensure headers match the configured DMO
- Frequent errors? Review the ingestion logs and retry after fixing
- Identity collision? Use confidence scores to manage conflicting identity matches
Frequently Asked Questions (FAQ)
📘 More Learning Resources
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