Case Study: Optimizing Data Infrastructure for Scalability and Cost Efficiency

Read on to see how we empowered data-driven growth!

About the Client

The client is a leading financial services company and one of Japan’s largest credit card issuers, serving over 20 million cardholders. Their mission is to deliver innovative financial solutions while ensuring operational efficiency and scalability.

The Challenge

Managing massive volumes of data across a microservices-based architecture presented significant challenges for the client. Their existing setup relied on AWS RDS Postgres, which struggled to meet growing business demands.

Analytical queries overloaded transactional systems, causing strain.
Inefficient storage and queries increased AWS Athena costs.
Growing data volumes caused delays and system performance issues.

The Nineleaps Solution

Nineleaps implemented a robust, structured solution to address these challenges, leveraging AWS technologies and automation for optimal performance.

Seamless Data Lake Integration

Migrated transactional data using AWS DMS into an S3-based data lake in parquet format, reducing storage requirements by 87% and enabling efficient, query-ready data access.

Cost-Effective Data Partitioning

Implemented automated date-based partitioning to optimize AWS Athena queries, significantly improving query speed and reducing processing costs by scanning only relevant partitions.

Automated File Merging

Built workflows with AWS Step Functions to consolidate small files into optimized parquet structures, drastically cutting S3 GET request costs while ensuring data consistency and minimal manual intervention.

Scalable Architecture

Designed a robust system to manage exponential data growth and increasing query loads seamlessly, ensuring consistent performance and scalability for future data requirements.

Real-Time Analytics

Facilitated seamless access to live, up-to-date data for business intelligence and analytics, ensuring reliable performance even during high-volume usage and critical operations.

Benefits and IMPACT

Our solution delivered measurable impact through optimized workflows, cost savings, and improved system performance

Streamlined Cost Savings

Reduced AWS Athena query costs by 90%, saving $500,000 annually while minimizing S3 GET request expenses.

Enhanced Query Performance

Date-based partitioning reduced query scan sizes, enabling faster data retrieval and improved efficiency.

Improved Data Accessibility

Optimized data availability for both historical and real-time analytics without disrupting transactional systems.

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