Migration

Migrating data from legacy systems to modern platforms or architectures is a complex process that requires careful planning and execution to ensure data integrity, consistency, and security.

• New platform Set up and automate provisioning capabilities using Terraform

  
  

• Legacy data, ETL & Pipelines, workflow migration

  
  

• Optimize configurations & address performance bottlenecks

By following the below steps and best practices, our team can successfully migrate data from legacy systems to modern platforms or architectures, enabling them to leverage the benefits of improved scalability, performance, and agility.

1. Assessment and Inventory: Conduct a comprehensive assessment of the legacy

   systems to identify all data sources, types, formats, and dependencies. Create an

   inventory of databases, files, applications, and other data repositories that need to be

   migrated.

2. Define Migration Strategy: Define a migration strategy based on factors such as the

   volume of data, complexity of the legacy systems, downtime constraints, and business

   requirements. Decide whether to perform a one-time migration or incremental migrations over time.

3. Data Profiling and Cleansing: Profile the data to understand its structure, quality, and

   consistency. Identify any data anomalies, duplicates, or inconsistencies that need to be

   addressed before migration. Perform data cleansing and transformation as necessary to

   ensure data integrity.

4. Select Migration Tools: Choose appropriate tools and technologies for data extraction,

   transformation, and loading (ETL). Consider factors such as compatibility with legacy

   systems, scalability, performance, and ease of use. Commonly used tools include

   Informatica, Talend, Apache NiFi, and custom scripts.

5. Data Extraction: Extract data from legacy systems using ETL processes or direct

   database connections. Extract data in batches to minimize the impact on production

   systems and ensure data consistency. Consider using incremental extraction techniques

   to capture only the changed data since the last migration.

6. Data Transformation: Transform the extracted data into the desired format and structure

   suitable for the target platform or architecture. Perform schema mapping, data type

   conversion, and data normalization as necessary. Implement business rules and logic

   during the transformation process.

7. Data Validation: Validate the transformed data to ensure accuracy, completeness, and

   consistency. Compare the migrated data with the source data to identify any discrepancies

   or errors. Implement data validation checks, integrity constraints, and reconciliation

   processes to verify data integrity.

8. Data Loading: Load the transformed data into the target system or architecture using ETL processes or bulk loading techniques. Monitor the data loading process to ensure optimal

   performance and resource utilization. Implement error handling and retry mechanisms to

   handle data loading failures gracefully.

9. Testing and Validation: Conduct comprehensive testing and validation of the migrated

   data to ensure that it meets the functional and non-functional requirements. Perform data quality assurance checks, regression testing, and user acceptance testing to validate the

   accuracy and completeness of the migrated data.

   
   

10. Deployment and Cutover: Deploy the migrated data into production environments and

    perform the cutover from the legacy systems to the new platform or architecture.

    Coordinate with stakeholders to minimize downtime and ensure a smooth transition.

    Implement rollback procedures in case of unexpected issues or failures.

11. Post-Migration Support: Provide post-migration support and assistance to users and

    stakeholders to address any issues or concerns related to the migrated data. Monitor the

    performance and stability of the target system and implement optimizations as necessary.

12. Documentation and Knowledge Transfer: Document the migration process, including

    the steps taken, challenges encountered, and lessons learned. Provide training and

    knowledge transfer sessions to relevant stakeholders to ensure ongoing maintenance and support of the migrated data

    .