Data Migration

This section sheds light on data migration, involving systematic transfer of data between systems, formats, or applications. It discusses its role in modernization, consolidation, and compliance, and introduces three main migration approaches: Big Bang, Trickle, and Strategic Hybrid with key challenges and suggestions for selecting the optimal migration strategy. A variety of IT scenarios rely on data migration, including:

• Modernization: Upgrading to newer hardware, software, or cloud-based platforms often involves data migration.
• Consolidation: Merging multiple data sources or applications necessitates data migration for streamlined management.
• Compliance: Adhering to evolving data regulations might require data migration to secure and centralized storage.
• Reporting: Advance reporting needs might require Power BI or similar tools. Data migration extracts data from DB2 and transfers it to reporting tools.

Accomplishments

1. Successfully served a client by employing our data migration project for a hybrid migration approach.
   • Utilized batch processing every 30th day to migrate differential data from DB2 to PostgreSQL.
   • Performed two iterations:
   • In the first iteration, migrated 137 tables with 4 million table rows, completing in approximately 1 hour and 45 minutes.
   • In the second iteration, with the updated 137 tables having around 1.4 million table rows, the migration was completed in 16 minutes.
   • The client performs this hybrid migration after every 30 days.
2. Completed a big bang migration project for one client, seamlessly transferring data from DB2 to SQL Server.
   • Performed SQL big bang migration with 1.8 million table row data.
   • Completed the migration in 25 minutes.
3. Completed a One-Time load and Incremental-load data migration project for one of the clients,
   • Data transferred from DB2 on iSeries to Cloud MySQL
   • Used Scott Klement’s JDBCR4 APIs to transfer the data
   • One-time data load completed for 800 Million records
   • Incremental-Load runs every 15 minutes and transfers new data
   • Daily load consists of 500,000 records

Approach

Several approaches can be employed for data migration, each with its unique advantages and considerations:

• Big Bang Migration: It is a well-planned and executed all-at-one approach that minimizes downtime and disruption, ideal for smaller datasets or less-critical systems.
• Trickle Migration: In this gradual, phased approach, data is migrated in smaller batches, minimizing initial impact but potentially extending the process overall. It is well-suited for large or complex datasets or situations where continuous system availability is paramount.
• Strategic Hybrid Migration: This strategic blend of Big Bang and Trickle methods enables rapid migration of non-critical data while deferring essential components to minimize disruption. It balances speed and risk mitigation effectively.

Key Challenges

Various challenges can arise during data migration projects, which require careful planning and mitigation strategies:

• Downtime and System Disruption: Minimizing downtime and disruptions to ongoing operations is vital. Thorough planning, testing, and contingency plans are necessary to limit impact.
• Data Inconsistency and Errors: Addressing data inconsistencies, ensuring consistency between source and target systems, and validating data accuracy at each stage is crucial for successful migration and maintaining data quality.
• Complexity and Resource Constraints: Data types, formats, and systems can vary during complex or large-scale migrations. Careful planning, resource optimization, and potential reliance on external expertise are essential for navigating these complexities.
• Data Validation and Verification: Rigorously validating data accuracy and consistency before, during, and after migration ensures data integrity and minimizes errors.
• Testing and Rollback Plans: Thorough testing of the migration process in a controlled environment and developing clear rollback plans are crucial for safeguarding data and enabling recovery if necessary.
• Documentation and Training: Creating comprehensive documentation of the migration process and providing training to relevant stakeholders can facilitate successful execution and minimize confusion.
• Data Transformation: As part of data transfer, it also needs to be transformed. The transformation rule should be carefully written.

Suggestions

The best data migration approach depends on numerous factors, including:

• Data volume: Larger datasets might favor trickle or hybrid migrations to minimize downtime.
• Downtime tolerance: Businesses sensitive to downtime might opt for staged or trickle migrations.
• The complexity of data: Complex data structures might require additional effort in transformation and validation, influencing the approach selection.
• Budget and resources: Complex approaches like hybrid migration might require more resources and planning compared to the Big Bang approach. The utility we have developed can perform all three applicable approaches.