Coforge

Who We Are

About Us Newsroom Leadership Partners Locations Careers Awards & Recognitions ESG Learn more about Coforge
Coforge: Where AI engineering meets industry expertise.

Learn about our company, our vision and values, and the 45,000+ professionals enabling businesses to harness the power of AI.

Learn more about Coforge
Composable Enterprise

CASE STUDY

Modernizing Data Integration with Aladdin Data Cloud and Cloud-Native Architecture

Industry

Asset Management

Our Contributions

Data Platform Modernization, Cloud Migration, API-led Integration

Technologies

Aladdin Data Cloud (Snowflake), AWS

Hero_AdobeStock_452878799
Coforge partnered with a leading global asset management firm to modernize its data integration ecosystem, supporting the Aladdin Investment Management platform. The engagement focused on simplifying a complex legacy data architecture, improving scalability, and enabling a cloud-native, future-ready data foundation.

By leveraging Aladdin Data Cloud and AWS-native services, Coforge transformed the client’s operational data landscape into a scalable, automated, and high-performance platform. The modernization enabled faster access to trusted data, reduced operational complexity, and empowered data-driven decision-making across investment operations.
Phase 1
Legacy platform assessment and architecture design
Phase 2
Cloud-native data platform implementation 
Phase 3
API-led integration and automation rollout 
Phase 4
Legacy decommissioning and optimization

Drag

Mid_AdobeStock_1541460692

The Challenge

The client’s legacy enterprise data warehouse, originally designed for historical audit data, had evolved into a complex operational integration platform. The addition of TIBCO BW middleware increased architectural complexity, making the system difficult to scale and maintain.

Rapid data growth led to performance bottlenecks and slower query response times, impacting business operations. Data integration across multiple upstream systems introduced inconsistencies, requiring significant manual effort for reconciliation and validation.

ETL processes became increasingly complex and time-consuming, delaying data availability and increasing operational risk. High maintenance overhead and dependency on legacy tools diverted resources from strategic initiatives. Additionally, onboarding new data sources was slow and error-prone, limiting agility and responsiveness to evolving business needs.

Our Approach

Cloud-Native Data Platform Architecture

 

Designed and implemented a scalable cloud-native architecture, enabling the decommissioning of legacy data warehouse systems and TIBCO BW middleware.

Automated Data Ingestion with Aladdin Data Cloud

 

Built AWS-based data pipelines integrated with Aladdin Data Cloud (Snowflake) for high-performance, scalable data ingestion and processing.

Streamlined ETL & Data Orchestration

 

Leveraged AWS Step Functions to orchestrate automated ETL workflows, reducing complexity and ensuring consistent, timely data availability.

API-Led Data Access Layer

 

Developed serverless, entity-level APIs (e.g., holdings, positions) using Node.js and AWS Lambda to enable secure, scalable data access for downstream systems.

Serverless & Low-Maintenance Architecture

 

Adopted serverless design principles to minimize infrastructure overhead and allow teams to focus on higher-value innovation initiatives.

 

Partner / Technology Ecosystem

 

  • Aladdin Data Cloud (Snowflake) 

  • AWS (Lambda, Step Functions, Data Lake)

  • Node.js APIs

  • Cloud-native integration frameworks

End_AdobeStock_625008312

Impact to Date

-40%

Reduction in Data Processing Time

-30%

Reduction in Operational Costs

+50%

Improvement in Data Quality & Consistency

-60%

Reduction in Manual Effort

Business Impact

 

  • Simplified IT landscape through decommissioning of legacy data warehouse and middleware

  • Reduced licensing and maintenance costs with cloud-native and serverless architecture

  • Improved data integrity through standardized ingestion and synchronization processes 

  • Enhanced operational efficiency with end-to-end automation and reduced manual intervention 

  • Accelerated onboarding of new data sources and applications 
  • Enabled faster, reliable access to investment data for improved decision-making