Business Impact

200% Improvement in Query Performance

Optimized Data Ingestion and ETL Design

Improved SLA for Bulk Requests

Efficient Tool Selection and Architecture Design

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

Case Studies

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