Performance analysis – MapR DB vs. Mongo DB – Tool Selection Process

Achieving low-latency API-based queries with Mongo DB

Case Study: Achieving Low-Latency API-Based Queries with MongoDB

About the Client

The client is a multi-national private banking and financial services company based out of India. With subsidiaries on a global platform, the client provides banking and financial products and services to retail and corporate customers.

The Business Challenge

The client wanted to build a secure and scalable No-SQL capable Data Mart to achieve low-latency API-based query response for its banking and financial applications.

What Aptus Data Labs Did

We helped the client find the right tool to achieve business requirements with high availability, scalability, reliability, and SLA. We modified and optimized ETL workflows and designed an ingestion mechanism for the Data Mart architecture. For analysis of performance, we selected two Market leaders, MapR-DB and Mongo DB, developed independent optimized APIs to connect to these environments and perform routine activities.

The Impact Aptus Data Labs Made

The solution helped the client select the best suitable tool, Mongo DB, for providing the least response time for API requests. Drawbacks as compared to MapR, i.e., slower bulk operations and ineffective batch processing, were handled by introducing multiple shards and replica environments with NVMe storage. The query performance was improved by 200% and low latency.


The Business and Technology Approach

Aptus Data Labs used the following methodology approach to plan, develop, and execute the workflow and solve existing challenges. Aptus Data Labs:

Architecture of MapR Hadoop

Tools Used

Big Data Platform

MapR Hadoop, Mongo DB, Python, Java

Testing & Automation

Bash scripting, SQL, Postman, Mongoose



The Outcome

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