Setup of a Cloud-Based Data Warehousing Solution on AWS for a South Asian Bank
Introduction
For Banks and large financial Institutions, consolidating data plays a crucial role in decision-making and strategic planning. One of our banking clients faced significant challenges in managing their scattered data infrastructure and they had a manual reporting process. The absence of a centralized data repository resulted in complex and inefficient reporting processes limiting the bank’s ability to generate timely insights. The bank’s data was dispersed across multiple sources including:
- Oracle databases
- MySQL databases
- Excel files stored locally and on shared drives
To overcome this, we successfully implemented a cloud-based data warehouse on AWS Redshift consolidating various data sources into one and transforming the bank’s data management capabilities.
Objective and Solution
Our goal was to:
- Migrate on-premise data to a secure, scalable cloud-based data warehouse on AWS Redshift.
- Establish AWS Redshift as the primary data storage hub for seamless reporting in the bank’s BI tool, Narrator.
Our implementation followed a structured approach including cloud setup ETL automation and business intelligence integration.
Cloud Infrastructure Setup
To build a secure and scalable cloud infrastructure, we configured:
- VPC (Virtual Private Cloud): Ensured secure networking within AWS.
- Amazon Redshift Serverless: Provisioned for centralized data-storage with efficient and scalable data analytics.
- IAM Users: Two IAM users were created with admin access for controlled permission management.
- EC2 Machines: Provisioned instances for hosting key applications such as Narrator BI, Power BI deployment, and testing & development environments.
ETL Workflow Automation
To streamline data migration and ensure data freshness, we implemented:
- AWS Glue Jobs: Automated ETL jobs that run daily, efficiently extracting data from on-premise sources.
- Batch Processing Using Spark: Managed data ingestion in batches to improve processing speed.
- Incremental and Full Load Strategies:
- Full Load: Small-to-medium tables are fully refreshed daily.
- Incremental Load: Large tables are processed incrementally to enhance performance.
Monitoring & Backups
To ensure data reliability and uptime:
- AWS CloudWatch: Monitored job execution and performance metrics.
- AWS Backup Service: Automated daily backups to safeguard critical data assets.
Business Intelligence Integration
To improve reporting and visualization we leveraged:
- Narrator BI Tool: Hosted on an EC2 machine, dynamically fetching data from Amazon Redshift for interactive reports.
- Power BI Deployment: Installed on an EC2 machine for advanced dashboard creation.
- Automated Python Script: This custom script runs daily, updating key financial data through a stored procedure in Redshift.
Benefits
The cloud-based data warehousing solution delivered remarkable improvements for the bank:
- Centralized Data Repository: All data sources were consolidated into AWS Redshift, streamlining access and improving data integrity.
- Enhanced Reporting Efficiency: Automated ETL pipelines reduced manual effort and accelerated report generation.
- Improved Decision-Making: Dynamic dashboards in Narrator and Power BI enabled data-driven insights for strategic planning.
- Data Security & Compliance: With AWS’s robust security features, sensitive financial data was protected against unauthorized access.
Conclusion
By implementing a scalable, cloud-based data warehouse solution on AWS Redshift we empowered the bank to streamline its reporting process and unlock valuable insights. This transformation enhanced data accessibility improved reporting timelines and enabled the bank to make smarter business decisions.
Our success in this project demonstrates our expertise in cloud data solutions and our commitment to delivering impactful results for financial institutions.
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