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Best Practices

Overview

Section titled “Overview”

Following best practices helps ensure your pipelines and workflows are maintainable, performant, and reliable. This guide covers recommendations for pipeline design, performance optimization, error handling, and maintenance.

Pipeline Design

Section titled “Pipeline Design”

Naming Conventions

Section titled “Naming Conventions”
  • Use descriptive names: Name pipelines, workflows, and transforms clearly
    • Good: Load_Sales_Data_From_API_To_Staging
    • Bad: Pipeline1 or Test
  • Be consistent: Use the same naming pattern across all pipelines
  • Include purpose: Names should indicate what the pipeline does

Structure and Organization

Section titled “Structure and Organization”
  • Single responsibility: Each pipeline should have one clear purpose
  • Modular design: Break complex processes into smaller, reusable pipelines
  • Logical flow: Arrange transforms in a logical, top-to-bottom flow
  • Group related transforms: Use visual grouping to organize related operations

Documentation

Section titled “Documentation”
  • Add notes: Document complex logic or business rules in transform notes
  • Version control: Use version control (Git) to track changes
  • Change logs: Document significant changes and reasons

Performance Optimization

Section titled “Performance Optimization”

Data Volume Management

Section titled “Data Volume Management”
  • Filter early: Apply filters as early as possible to reduce data volume
  • Select only needed fields: Don’t read unnecessary columns
  • Use row limits for testing: Limit rows during development and testing

Database Operations

Section titled “Database Operations”
  • Use bulk loaders: For large datasets, prefer bulk loaders over row-by-row inserts
    • Use Bulk Loader or MySQL Text Loader for MySQL
    • Use Indexed Table Output with appropriate commit sizes
  • Batch commits: Set appropriate commit sizes (e.g., 1000-10000 rows)
  • Index optimization: Ensure target tables have appropriate indexes
  • Connection pooling: Reuse database connections when possible

Transform Optimization

Section titled “Transform Optimization”
  • Minimize transformations: Reduce the number of transforms when possible
  • Use SQL when appropriate: For complex filtering or joins, consider using SQL transforms
  • Avoid unnecessary sorts: Only sort when absolutely necessary
  • Cache lookups: Cache lookup results when the lookup data doesn’t change

Memory Management

Section titled “Memory Management”
  • Stream processing: Process data in streams rather than loading everything into memory
  • Split large files: For very large files, consider splitting into smaller chunks
  • Monitor memory usage: Watch for memory issues during execution

Error Handling

Section titled “Error Handling”

Input Validation

Section titled “Input Validation”
  • Validate data types: Ensure data types match expected formats
  • Check for nulls: Handle null and empty values appropriately
  • Validate required fields: Verify required fields are present
  • Range checks: Validate numeric values are within expected ranges

Error Logging

Section titled “Error Logging”
  • Log errors: Configure error logging to capture issues
  • Include context: Log enough information to diagnose problems
  • Error tables: Consider writing errors to dedicated error tables
  • Alerting: Set up alerts for critical errors

Recovery and Retry

Section titled “Recovery and Retry”
  • Idempotent operations: Design pipelines to be safely rerunnable
  • Checkpointing: Use timestamps or checkpoints for incremental loads
  • Retry logic: Implement retry logic for transient failures
  • Rollback capability: Plan for rollback in case of failures

Variables and Configuration

Section titled “Variables and Configuration”

Variable Usage

Section titled “Variable Usage”
  • Parameterize everything: Use variables for file paths, connection strings, table names
  • Environment-specific: Use different variable sets for dev, test, and production
  • Naming conventions: Use clear, consistent variable names
  • Documentation: Document what each variable is used for

Security

Section titled “Security”
  • Never hardcode credentials: Always use variables or secure storage for passwords
  • Encrypt sensitive data: Encrypt passwords and sensitive configuration
  • Limit access: Restrict access to production configurations
  • Audit access: Log access to sensitive configurations

Testing

Section titled “Testing”

Development Testing

Section titled “Development Testing”
  • Test incrementally: Test each transform as you add it
  • Use sample data: Start with small, representative datasets
  • Validate outputs: Verify output data matches expectations
  • Test edge cases: Test with empty data, null values, boundary conditions

Integration Testing

Section titled “Integration Testing”
  • End-to-end tests: Test complete pipeline flows
  • Data validation: Compare output with expected results
  • Performance testing: Test with production-like data volumes
  • Error scenario testing: Test error handling and recovery

Maintenance

Section titled “Maintenance”

Monitoring

Section titled “Monitoring”
  • Execution logs: Regularly review execution logs
  • Performance metrics: Monitor execution times and resource usage
  • Data quality: Check data quality metrics
  • Error rates: Track and analyze error rates

Updates and Changes

Section titled “Updates and Changes”
  • Version control: Always use version control for pipelines
  • Change management: Follow a proper change management process
  • Backup before changes: Backup pipelines before making significant changes
  • Test in non-production: Always test changes in non-production first

Documentation

Section titled “Documentation”
  • Keep documentation updated: Update documentation when pipelines change
  • Document dependencies: Note dependencies between pipelines
  • Business logic documentation: Document complex business rules
  • Run instructions: Document how to run and schedule pipelines
Section titled “Related”