Our client is a leading financial institution with over 15 years of experience, consolidating its position as a top-tier general license bank. The institution offers a wide range of financial products and services for both individuals and corporations. With a strong network of physical branches and a rapidly expanding digital presence, its strategic vision is to strengthen its industry leadership through constant innovation and an optimized customer experience.
Facing a massive volume of monthly transactions, the institution recognized that achieving its growth objectives required a fundamental shift in its data strategy. This is a benchmark case study in data warehouse modernization for the banking sector.
What was the challenge?
To maintain its competitive edge, our client needed a data platform that could support strategic decision-making. However, their existing on-premise Data Warehouse (DWH), built on traditional Microsoft SQL Server 2019 technologies, presented significant challenges:
- Scalability and flexibility: The legacy infrastructure limited their ability to scale, preventing agile and efficient data management.
- Inefficient ETL processes: Data dispersion and the lack of a clear Organizational Data Store (ODS) led to inefficient ETL processes built on SQL Server Integration Services (SSIS), making it difficult to implement robust data governance and quality practices.
- Competitive risk: Without action, the bank risked falling behind due to limited analytical capabilities. The dependency on proprietary Microsoft technologies created interoperability issues and increased the risk of errors.
- Security and compliance gaps: The lack of formal development lifecycles, versioning, and adequate access controls posed significant maintenance, quality, and security risks to confidential information. This made achieving financial services data compliance on AWS a top priority.
The clear path forward was a comprehensive on-premise to cloud data migration.
The solution: A strategic SQL server to redshift migration
To address these challenges, Pragma designed and executed a phased modernization plan. After a thorough discovery and diagnosis phase to understand the client's strategic vision and existing architecture, we identified critical pain points and business needs.
Our solution was the construction of an enterprise AWS Data Lakehouse for Banking. This modern architecture provides a secure, cost-effective, and fault-tolerant method for integrating data from diverse sources, empowering the organization to become truly data-driven. The core of the project was a strategic SQL Server to Redshift migration, transitioning from a restrictive on-premise environment to a flexible and powerful cloud platform.
The new architecture includes:
- Amazon S3 for raw and staged data storage in the data lake.
- Amazon Redshift Serverless as the analytical data warehouse for curated data marts.
- AWS Glue for ETL automation, replacing inefficient SSIS workflows with managed and automated data integration pipelines.
- AWS Glue Data Catalog for metadata management, a crucial step to improve data governance in banking.
This hybrid approach combines the flexibility of a data lake with the analytical power of a data warehouse, creating a robust and scalable analytics platform for financial reporting.
The results: Enhanced analytics, governance, and business intelligence
Our work delivered a significant impact, establishing a modern cloud data platform for financial services that yielded measurable outcomes:
- Centralized and scalable storage: Amazon S3 was established as the central data repository, providing superior scalability, durability, and flexible data access.
- Modernized Data Warehouse: Amazon Redshift modernized the analytics layer, leveraging powerful processing capabilities and seamless integration with the data lake via Redshift Spectrum.
- Automated ETL processes: AWS Glue replaced legacy SSIS jobs with automated, maintainable, and efficient data integration workflows.
- Empowered business intelligence: The new data warehouse provides clean, filtered, and governed data, empowering BI tools and analysts through AWS Glue Data Catalog and Amazon Athena.
- Foundation for a 360° view: The enterprise data lakehouse provides the foundation for building a customer 360 view in financial services, consolidating structured and unstructured data to enable more robust, real-time reporting and analytics.
How AWS was used within the solution
The solution leverages a suite of key AWS services, delivered using DevOps best practices, including Infrastructure as Code (IaC) with CloudFormation and CI/CD pipelines.
- Data Lake & warehouse: Amazon S3, Amazon Redshift Serverless.
- Data integration & cataloging: AWS Glue, AWS Glue Data Catalog.
- Ad-hoc querying: Amazon Athena.
- Governance & security: AWS Lake Formation.
- Data migration: AWS DataSync.
- Monitoring & quality: Amazon CloudWatch, CloudTrail, and integration with Great Expectations to ensure data quality and observability.
This robust delivery model provided the client with a secure and powerful solution aligned with the AWS Well-Architected Framework, completing a successful Data Warehouse modernization for financial services.
