Business Objective / Goal
To build a secure, scalable, and low-latency NoSQL-capable Data Mart that supports high availability and SLA-driven performance for real-time API-based financial applications.
Solutions & Implementation
- Aptus Data Labs conducted a comparative performance analysis of two leading NoSQL technologies, MapR DB and Mongo DB, to determine the best fit for the client’s Data Mart.
- Independent, optimized APIs were developed for both platforms to perform benchmark testing of query response times and bulk operation throughput.
- Mongo DB was selected for its superior performance in API-based querying, despite its limitations in bulk operations, which were mitigated using NVMe storage, replica sets, and sharding strategies.
- A fault-tolerant, disaster recovery-ready architecture was designed, and the ingestion layer was optimized to ensure performance and scalability.
- Monitoring environments and automated alarm scripts were implemented to ensure system health and availability.
- The final deployment enabled high-speed data access, improved SLA compliance, and a flexible architecture for future scale-out.
Major Technologies Used
- MapR Hadoop, Mongo DB – Core database platforms under comparison
- Python, Java, SQL, Bash scripting – For scripting, automation, and API development
- Postman, Mongoose, JMonitor – For API testing, database interaction, and performance monitoring
Business Outcomes
- 200% Improvement in Query Performance Query performance increased by 200%, resulting in significantly faster API response.
- Optimized Data Ingestion and ETL Design Optimized API and ingestion design, allowing the client to process large volumes of data efficiently.
- Improved SLA for Bulk Requests Bulk request SLA improvement through caching and optimized storage architecture.
- Efficient Tool Selection and Architecture Design Strategic technology adoption guided by data-driven tool evaluation.
