Letting Data Speak!
Case Study
Modern Data Warehouse Implementation for Payment Processing Company
About the Client
A leading payment processing company that provides a complete suite of solutions for Automated Clearing House and Card payments processing, verification, and recovery. The company processes large volumes of financial transactions with an online transaction processing (OLTP) database containing nearly 300 million records in the ACH database and around 80 million records in the card database, totaling 5TB of data.
Challenge
The client was experiencing significant performance issues with their reporting capabilities due to the lack of a dedicated reporting database. They were using a read-only replica of their OLTP database for reporting purposes, which created bottlenecks and inefficiencies. The company needed a well-designed modern data warehouse architecture to support their web portal reporting capabilities and handle the massive volume of ACH and Card transaction data effectively. The existing infrastructure could not provide the on-demand reporting capabilities required for their growing business needs.
Key Results
Improved reporting performance by 70% through dedicated data warehouse implementation
Successfully migrated 5TB of existing data (380 million records) with zero data loss
Enhanced system scalability to handle growing transaction volumes
Solution
The solution involved designing and implementing a comprehensive data warehouse architecture hosted on Amazon Web Services (AWS). The implementation included a one-time bulk migration of existing ACH and Card data totaling 5TB from the OLTP system to the new Redshift data warehouse.
A robust ELT (Extract, Load, Transform) pipeline was established using AWS Data Migration Service (DMS) replication jobs to periodically transfer data from the OLTP database to the Redshift clusters. This ensured real-time data availability for reporting while maintaining the integrity of the transactional systems.
The solution incorporated CloudWatch metrics for comprehensive monitoring and performance tracking of the data warehouse operations. Scheduled ELT jobs were configured to automatically synchronize data between the OLTP system and the data warehouse, eliminating the need for manual data transfers and reducing the load on the primary transactional database.
The new architecture separated reporting workloads from operational systems, allowing for optimized query performance and scalable analytics capabilities without impacting transaction processing performance.
Technologies Used
Amazon Redshift
AWS Data Migration Service (DMS)
Amazon CloudWatch
AWS S3 (for data staging and backup)
SQL and ETL/ELT processes
Data warehouse modeling and design
AWS IAM (for security and access control)
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