Performance analysis – MapR DB vs. Mongo DB – Tool Selection Process
MapR and Mongo DB benchmarking for tool selection for a BFSI company
About the Client
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:
- Designed the fault-tolerant and scalable architecture with DR
- Implemented APIs for related data operations
- Implemented monitoring environment and automated alarm scripts
- Designed data collections and configuring shards
- Performed jMonitor testing
- Tested use-cases and analyzed performance
- Performed Shard and Replica failure testing
Architecture of MapR Hadoop
Tools Used
Big Data Platform
MapR Hadoop, Mongo DB, Python, Java
Testing & Automation
Bash scripting, SQL, Postman, Mongoose
Monitoring
Jmonitor
The Outcome
- Verified performance for single, multiple, and bulk operations
- Query performance improved by 200%
- Improved SLA for bulk requests by implementing the cache
- Adopted new technology – MapR-DB, Mongo DB
- Optimized design
- Developed efficient API for requests
- Improved Data Ingesting process
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