Azure Data Factory Pipelines: Best Practices
Master pipeline design with proven patterns for reliable, scalable data integration in Azure Data Factory.
Azure Data Factory (ADF) is the cloud-based ETL/ELT service that allows you to create data-driven workflows for orchestrating and automating data movement and transformation. After building dozens of pipelines across insurance, rail, and manufacturing, I've distilled the patterns that actually work in production.
Key Pipeline Design Patterns
1. Use Parameters for Flexibility
Never hardcode connection strings, container names, or file paths. Use pipeline parameters and pass them through your deployment pipeline.
2. Implement Proper Error Handling
Configure retry policies and use the Execute Pipeline activity with failure branches to handle transient failures gracefully.
3. Optimize for Cost and Performance
- Use Self-Hosted Integration Runtime for on-premises data when appropriate
- Batch your data movement operations
- Use Copy Data activity's parallel copy feature for large datasets
Conclusion
Following these best practices will help you build pipelines that are maintainable, scalable, and cost-effective. Stay tuned for deeper dives into each pattern.
