Industry : Financial Services / Enterprise Finance Operations
Duration : 2 Years 7 Months
Reports Delivered : 250+
A global enterprise partnered with DataTerrain to modernize its financial data ecosystem and eliminate data silos across multiple finance systems.
The initiative focused on migrating diverse financial data sources — including OFA, VPS, USL, Lease, Tririga, AP, AR, GL, Fixed Assets, and Tram — into a centralized Finance Data Vault (FDV) on AWS Redshift.
The project’s primary goal was to create a unified, automated, and auditable finance data warehouse that could streamline month-end reconciliation through MACE (Matching and Clearing Engine), enabling finance teams to close books faster with full confidence in data accuracy.
The client’s legacy finance infrastructure consisted of multiple disparate systems. These fragmented data silos led to:
To overcome these challenges, the organization needed a scalable cloud data platform that could support real-time financial analytics, automated reconciliation, and centralized reporting — all within a secure AWS ecosystem.
DataTerrain implemented an end-to-end finance data migration and integration solution leveraging AWS Redshift, Finance Data Vault (FDV), and AWS EMR for high-volume data processing.
Project Highlights
| Parameter | Details |
|---|---|
| Source Systems | OFA, VPS, USL, Lease, Tririga, AP, AR, GL, Fixed Assets, Tram, and others |
| Target Systems | FDV (Finance Data Vault) and AWS Redshift |
| Reports Delivered | 250 |
| Technology Stack | AWS EMR 6.6.0 & 7.3.0, Redshift Query Editor V2, Cradle, PySpark, Spark SQL, Scala, DynamoDB |
| Conversion Approach | Data transformation and reconciliation automation using AWS-native tools |
DataTerrain engineers developed robust ETL pipelines using PySpark and Spark SQL to migrate financial data from multiple legacy systems into FDV and Redshift.
This unified data model enabled consistent reporting, traceability, and faster reconciliation, serving as the foundation for the organization’s Finance Data Lakehouse strategy.
After migration, DataTerrain integrated the processed datasets into MACE (Matching and Clearing Engine), enabling automated month-end close reconciliation.
This replaced traditional manual processes with intelligent matching algorithms, improving accuracy and saving significant operational time.
To meet user-specific requirements, DataTerrain’s development team:
This agile engagement ensured business flexibility without compromising technical rigor.
Ensuring data reliability was critical to project success. DataTerrain implemented a multi-layer quality framework that included:
This ensured high-confidence, audit-ready data that met enterprise compliance standards.
The migration to AWS Redshift and Finance Data Vault transformed the client’s financial reporting and reconciliation process into a modern, data-driven environment.
Key Outcomes:
This modernization initiative strengthened the organization’s data governance posture, reduced operational complexity, and provided the agility needed for strategic financial decision-making.
With a modern financial data foundation in place, the enterprise plans to extend automation by introducing:
DataTerrain continues to partner closely with the client to scale its AWS cloud analytics environment, ensuring continuous innovation and measurable business value.
DataTerrain is a global technology partner specializing in data migration, business intelligence modernization, and analytics automation.
With deep expertise in AWS Redshift, Snowflake, Power BI, and Finance Data Vault architectures, DataTerrain helps enterprises modernize legacy financial systems, improve governance, and unlock value from their data.
Empowering finance transformation through cloud data engineering, automation, and analytics excellence.