Multi-country ERP data migration
Led ERP data migration across Austria, Switzerland, the Netherlands, and North America into Oracle Fusion.
Business problem
Business was modernizing its ERP system and needed to migrate historical data from multiple legacy systems into Oracle Fusion, while ensuring data quality, compliance, and minimal downtime.
Functional requirements
Legacy ERP systems are not suitable for modern growth and analytics. The migration needed to ensure data integrity, support multiple countries' data, and provide a framework for future migrations.
Architecture overview
The architecture involved using Azure Data Factory for orchestration, Informatica IDMC for data integration and quality, PySpark for transformations, and SQL Server for staging and validation. Power BI was used for reporting and monitoring.
Technology stack
Azure Data Factory, Synapse Analytics, Informatica IDMC (CAI, CDI, CDQ), PySpark, SQL, SSMS, Power BI.
Data flow
Data was extracted from multiple legacy ERP systems, transformed using PySpark and Informatica, and loaded into Oracle Fusion. The process included data validation, cleansing, and enrichment to ensure high-quality migration.
Design decisions
The decision to use Azure Data Factory and Informatica IDMC was based on their capabilities for handling complex data integration scenarios, support for multiple data sources, and robust data quality features. PySpark was chosen for its scalability and performance in processing large datasets.
Security considerations
Data security was a top priority, with encryption in transit and at rest, role-based access controls, and compliance with data protection regulations across all countries involved in the migration.
Performance optimization
The pipeline was optimized by parallelizing data extraction and transformation processes, tuning PySpark jobs for better performance, and implementing incremental data loads to reduce processing time. Performance metrics were monitored to ensure efficiency.
Scalability considerations
The architecture was designed to handle increasing data volumes and additional countries in the future. The use of cloud-based services like Azure Data Factory and Synapse Analytics allows for easy scaling of resources based on demand.
Cost optimization
Cost optimization strategies included leveraging Azure's pay-as-you-go model, optimizing data storage and processing costs, and using reserved instances for predictable workloads. Regular cost reviews were conducted to identify areas for further savings.
Monitoring & logging
Monitoring and logging were implemented using Azure Monitor and Power BI dashboards to track pipeline performance, data quality metrics, and error rates. Alerts were set up for critical failures to ensure timely intervention.
Error handling
Error handling mechanisms included retry policies for transient failures, detailed logging of errors for troubleshooting, and automated notifications to the development team for immediate action. Data validation checks were also in place to catch inconsistencies early in the process.
Testing strategy
The testing strategy involved unit testing of individual components, integration testing of the entire pipeline, and user acceptance testing with stakeholders. Test cases were designed to cover various scenarios, including edge cases and failure conditions.
Deployment process
The deployment process followed a CI/CD approach using Azure DevOps pipelines. Changes were version-controlled, and automated builds and deployments were triggered upon code commits. Staging environments were used for testing before production deployment, ensuring a smooth transition with minimal downtime.
Future enhancements
Future enhancements include implementing machine learning models for predictive data quality checks, expanding the migration framework to support additional legacy systems, and integrating more advanced analytics capabilities into the reporting dashboards.
Lessons learned
Key lessons learned include the importance of thorough data profiling before migration, the need for clear communication and collaboration among cross-functional teams, and the value of continuous monitoring and optimization to maintain high performance and data quality throughout the migration process.