What's a data migration?

Data migration involves transferring data between systems, formats, or storage types, often during system upgrades, cloud adoption, or database modernization. It requires careful planning to ensure accuracy, consistency, and minimal downtime, with key steps including assessment, extraction, transformation, validation, and loading. Common challenges include data integrity, compatibility, and performance optimization.

Core Technical Concepts/Technologies

• ETL (Extract, Transform, Load)
• Data Mapping
• Schema Conversion
• Incremental vs. Full Migration
• Validation and Testing
• Downtime Mitigation Strategies

Main Points

• Purpose: Data migration is critical for system upgrades, cloud transitions, or database consolidation.
• Process:
  • Assessment: Analyze source/target systems, data dependencies, and business rules.
  • Extraction: Pull data from source systems, often using batch or streaming methods.
  • Transformation: Cleanse, normalize, and map data to the target schema.
  • Validation: Ensure data accuracy via checksums, sampling, or reconciliation.
  • Loading: Load data into the target system, with options for bulk or incremental updates.
• Challenges: Data corruption, schema mismatches, and performance bottlenecks.
• Tools: ETL tools (e.g., Apache NiFi, Talend) or custom scripts (Python, SQL).

Technical Specifications/Implementation

• Example Workflow:

  # Pseudocode for incremental migration  
  def migrate_incremental(source_db, target_db, last_run_timestamp):  
      new_data = source_db.query(f"SELECT * FROM table WHERE updated_at > {last_run_timestamp}")  
      transformed_data = transform(new_data)  # Apply mapping rules  
      target_db.bulk_insert(transformed_data)  
  

• Validation SQL:

  -- Compare row counts between source and target  
  SELECT COUNT(*) FROM source_table  
  EXCEPT  
  SELECT COUNT(*) FROM target_table;  
  

Key Takeaways

1. Plan Thoroughly: Assess data dependencies and business logic early to avoid surprises.
2. Test Rigorously: Validate data post-migration with automated checks and manual sampling.
3. Minimize Downtime: Use incremental migration or parallel runs for critical systems.
4. Document Everything: Maintain logs of mappings, transformations, and validation results.

Limitations/Further Exploration

• Real-Time Migration: Complex for high-velocity data (e.g., IoT streams).
• Legacy Systems: May require custom connectors due to outdated APIs/formats.
• Cost: Cloud migrations can incur unexpected egress/processing fees.