dlt Best Practices
Source Design
Use Generators for Memory Efficiency
Why: Generators allow dlt to process data in chunks, reducing memory usage.
Implement Proper Pagination
Use Incremental Loading
Benefits:
- Faster pipeline runs
- Lower API costs
- Reduced database load
- Lower data transfer costs
Choose Appropriate Write Disposition
Handle Rate Limits
Schema Management
Define Schema for Critical Fields
Why:
- Ensures correct data types
- Prevents schema drift issues
- Documents data structure
- Catches data quality issues early
Handle Nested Data Appropriately
Version Your Schemas
Configuration Management
Use Configuration Hierarchy
Priority order (highest to lowest):
- Code (inline)
- Environment variables
.dlt/secrets.toml.dlt/config.toml
Best practice: Use each for its purpose
Separate Environments
Or use profiles:
Manage Secrets Securely
Performance Optimization
Batch Your Data
Impact: 100x faster loading (1000 batches vs 100,000 individual inserts)
Use Appropriate Data Types
Benefits:
- Smaller storage size
- Faster queries
- Correct sorting and filtering
- Better compression
Limit Data Transfer
Parallel Processing
Error Handling
Implement Proper Retry Logic
Validate Data
Monitor Pipeline Health
Testing
Unit Test Your Sources
Integration Testing
Test with Sample Data
Production Deployment
Use a Dedicated Pipeline Name
Why: Separate state for different pipelines, easier monitoring
Implement Health Checks
Set Resource Limits
Monitoring and Alerting
Cost Optimization
Minimize API Calls
Reduce Warehouse Costs
Compress Data
Security Best Practices
Never Commit Secrets
Use Least Privilege
Rotate Credentials
Ready for production? Check out Use Cases for real-world scenarios!