Financial Institution Accelerates Data Performance & Cuts Costs with AWS Redshift Migration
At a glance
A specialized lending financial institution in Canada was expanding its data footprint and needed to migrate away from Teradata and Oracle to reduce costs and improve performance. New Math Data designed and delivered a cloud-native migration to AWS Redshift, rebuilding complex batch workflows using Glue Jobs, Glue Workflows, Step Functions, Lambda, and EventBridge. The result was a secure, production-ready platform that cut data processing time by 30%, eliminated costly licenses, and delivered over $1 million in annual savings without any downtime.
Industry
Use Case
Migration from legacy Teradata environment to AWS Redshift for improved cost, speed, and scalability
Solution implemented
- Migrated all data from Teradata into a Data Lake on AWS powered by AWS Redshift
- Recreated legacy batch workflows from Informatica and ActiveBatch in AWS using Glue Jobs, Glue Workflows, Step Functions, Lambda, and EventBridge
- Built custom pre-processing logic to manage complex data types and constraints
- Implemented data-type mapping and transformation outside standard AWS migration tooling for greater precision and control
- Delivered hands-on training in AWS CDK, Python (boto3), and GitHub source control for customer teams
The value equation
- 30% reduction in daily data-processing time
- 35% reduction in overall data management costs
- Over $1 million in annual savings in licensing costs
- Seamless transition with zero downtime
- Improved data accuracy, consistency, and maintainability
Company Snapshot
A specialized lending financial institution based in Canada.
Location
Canada
Modernizing a Legacy Teradata Environment Reduces Data Processing Time by 30%
New Math Data worked closely with the customer’s cloud architects to develop a safe, reliable migration approach suited to their compliance and operational needs.
Problem
The institution’s existing data warehouse stack, comprising Informatica, ActiveBatch, and Teradata, was struggling to handle the increased data volume, becoming costly and difficult to maintain. Mounting licensing and storage fees, combined with poor data quality and limited flexibility, made modernization a business imperative. Previous non-production migration trials failed to achieve a complete cutover, leaving continuous data flowing into the legacy system and creating dual maintenance overhead.
The organization needed a migration approach that could:
- Safely transition data at scale without downtime
- Accurately handle complex data types and constraints
- Recreate legacy batch workflows in AWS
- Maintain compliance within a regulated financial environment
Solution
Custom Migration Approach
A custom solution outside standard AWS tooling was required to achieve precise control over data-type mappings and transformations. The team implemented tailored preprocessing pipelines to handle source data complexity and ensure accuracy during migration.
Cloud-Native Workflow Recreation
Using AWS Glue Jobs, Glue Workflows, Step Functions, Lambda, and EventBridge, the legacy batch processes were re-engineered in AWS to maintain operational continuity. This ensured that daily data workflows continued without interruption throughout the migration.
Seamless Data Transition
Data was securely migrated from Teradata into a Data Lake on AWS powered by AWS Redshift, preserving integrity and ensuring consistent schema handling across environments. The solution prevented Teradata downtime and maintained compliance with enterprise security practices.
Training and Knowledge Transfer
New Math Data provided training on Redshift user/workload management and AWS Glue ETL/Spark to ensure the team could operate, extend, and maintain the new platform independently.
Over the course of the engagement, New Math Data delivered a complete, production-ready migration that eliminated legacy dependencies, improved data processing efficiency, and dramatically reduced operating costs—establishing a modern, scalable data platform on AWS Redshift.
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