Cloud Data Migration

Challenge:

The client needed to modernize their UK analytics platform by migrating from legacy technologies such as DB2, Informatica, MicroStrategy, SQL Server, SSIS, SSAS and SSRS to Azure Cloud. The objectives were to create a scalable cloud-based solution, reduce reliance on outdated systems and maintain analytics capabilities without disrupting critical business operations.

Solution:

Using migVisor tools, EPAM conducted a detailed assessment of 150 Informatica workflows, 1,200 SQL procedures, 1,000 DB2 tables and 2,000+ MicroStrategy reports. Instead of directly migrating all reports, a Self-Service Analytics framework was implemented — redesigning MicroStrategy reports and Informatica ETL processes into Data Products based on migVisor’s data lineage mappings. Automation tools were used to convert Informatica workflows into Python and Snowflake SQL, while the DB2 data warehouse and OLAP cubes were migrated into Snowflake as tables and views.

Key Outcomes:

• 30% Scope Reduction: Streamlined migration scope enabled faster delivery and minimized costs.

• Decommissioned Legacy Systems: Fully eliminating old technologies reduced license costs and improved ROI.

• Automation Results: Automated workflow and SQL logic conversion reduced migration time by 30%, delivering on time and within budget. Automated UAT (User Acceptance Testing) ensured smooth data validation and production readiness.

Challenge:

The client sought to modernize their Supply Chain planning platform by migrating from an Exadata backend to a modern data warehouse solution. Objectives included aligning with broader business goals, reducing operational costs and defining a clear migration strategy. The existing system featured 11 databases, 6,600+ Exadata objects, over 5,000 tables and views, 1,657 Informatica flows, 490 stored procedures, 280 jobs and 9.3 TB of historical data, highlighting the sheer scale and complexity of the mission.

Solution:

In collaboration with Databricks and Microsoft, EPAM designed and delivered a Minimum Viable Product (MVP) on Azure Databricks. The migVisor Suite was utilized to scan the entire Exadata environment, assess complexity, identify dependencies and prioritize workloads. The migration involved transitioning 9.3 TB of historical data and validating the new Databricks architecture with real workloads, ensuring full functionality.

Key Outcomes:

• Simplified Migration Planning: Analyzed 6,600+ database objects, 1,657 ETL flows and 280 jobs for greater accuracy and efficiency.

• Focused Migration Strategy: Delivered an 18-month roadmap targeting 60% of Exadata objects and 33% of ETL flows for initial phases.

• Data Modernization: Established a unified Databricks Data Warehouse while reducing manual effort and ensuring cost transparency.

• Effort Estimation: migVisor automation provided clear cost and effort projections to streamline decision-making.

Challenge:

The client aimed to migrate their data platform from Exasol to AWS to enhance scalability, query performance and integration with modern cloud services. The objectives included:

• Migrating the fast-serving data layer to AWS Redshift.

• Adopting AWS Managed Workflows for Apache Airflow (MWAA) for workflow orchestration.

• Upgrading to Databricks Unity Catalog.

• Improving onboarding processes and developer experiences.

A seamless, low-risk transition required a deep understanding of data structures, object relationships, reports and automation to maintain operational integrity.

Solution: EPAM utilized migVisor’s automated assessment tools to analyze the Exasol environment and guide the migration. Key steps included:

• Conducting automated scanning, dependency mapping and object profiling to inform the target architecture design.

• Migrating ingestion and transformation processes to AWS Redshift while maintaining data replication for consistency.

• Redirecting BI reports to the new access layer to ensure minimal disruption.

• Leveraging post-migration reconciliation with migVisor to ensure data integrity and a smooth cutover.

Key Outcomes:

• Improved Scalability: Transitioned to a fully cloud-native solution with AWS Redshift, enabling better performance and greater flexibility.

• Modernized Workflows: Adopted AWS Airflow and Databricks Unity Catalog to streamline operations and enhance developer experiences.

• Seamless Transition: Automated tools minimized risk and ensured data consistency throughout the migration process with post-migration validation.