A bank’s analytics transformation journey —
Automated assessment and transformation of Informatica workflows and Oracle EDW to AWS

A Top 100 American bank used LeapLogic for intelligent assessment and auto-converting ~80% of their Informatica and Oracle data warehouse to an enterprise-ready AWS cloud-native system. The transformation leveraged EMR Spark and Amazon Redshift and instituted a CI/CD cloud-native DevOps architecture and framework implementation.

Business requirements

A Top 100 American bank wanted to migrate its Oracle data warehouse and Informatica workflows to AWS to reduce capital and operational expenses while leveraging real-time analytics to improve customer satisfaction. Lacking in-house expertise for this critical transformation, it sought a partner to facilitate cloud migration and ensure consistent data security and compliance.

Specific business requirements included:

• Validating the transformation scope
• Identifying gaps and gathering information about technical debt, dependencies, and performance recommendations
• Providing a detailed inventory and complexity analysis report covering the current state process, functional mapping, and an implementation roadmap
• Meeting security and compliance needs throughout the transformation process

Solution

The Impetus team used LeapLogic, an automated cloud migration accelerator, to assess the bank’s Informatica workflows and Oracle environment. This identified the migration scope and provided a detailed report on future architecture, phased transformation roadmap, and recommended security tools/processes.

LeapLogic auto-converted 7 complex Informatica ETL scripts with over 30,000 lines of code (~80% of Oracle and Informatica code) to AWS (EMR Spark, Redshift), retaining existing business logic and process flows. The migration included:

• 3,000 target tables for ETL and user consumption
• ~1,300 reports and ~450 Informatica graphs

LeapLogic enabled workflow, mapping, and transformation-level parallelism, repointing queries from Tableau, SSRS, and OBIEE to Redshift. It also performed cell-by-cell automated validation for all transformed workloads to identify potential issues in legacy codes with limited records.

Impact

• Ensured 54% improvement in data ingestion – 5.5 hours vs. 12+ hours
• 93% improvement with Tableau extracts on Amazon Redshift vs. Oracle
• Established frameworks for cloud-native CI/CD and data governance

Impetus also established a data catalog system on AWS for data governance and planned end-to-end maintenance of the infrastructure on AWS. It used an information lifecycle management framework to bridge corporate and IT functions.