Challenge
The client required a high-performance analytics application capable of handling millions of records and delivering near real-time query results.
.
Key challenges:
Designing queries optimized for extremely large data sets.
Ensuring application performance at scale without compromising accuracy.
Supporting the rapid development of new features while maintaining system stability.
The client required a high-performance analytics application capable of handling millions of records and delivering near real-time query results.
.
Key challenges:
Designing queries optimized for extremely large data sets.
Ensuring application performance at scale without compromising accuracy.
Supporting the rapid development of new features while maintaining system stability.
Solution
To address these challenges, the development team implemented a comprehensive refactoring and optimization strategy:
Moved data from MongoDB to PostgreSQL to achieve faster query performance.
Optimized service architecture using microservices and microfrontend principles.
Achieved 100% unit test coverage, ensuring reliability and reducing regression risks.
Introduced Kafka for asynchronous processing and Redis for caching.
To address these challenges, the development team implemented a comprehensive refactoring and optimization strategy:
Moved data from MongoDB to PostgreSQL to achieve faster query performance.
Optimized service architecture using microservices and microfrontend principles.
Achieved 100% unit test coverage, ensuring reliability and reducing regression risks.
Introduced Kafka for asynchronous processing and Redis for caching.
Results
Designed queries optimized for extremely large data sets.
Ensured application performance at scale without compromising accuracy.
Supported the rapid development of new features while maintaining system stability.
Our team migrated the system from MongoDB to SQL, optimized queries, and refactored the backend using microservices. With 100% unit test coverage, the platform now provides faster insights, continuous validation of security controls, and integration with vulnerability intelligence sources.
Designed queries optimized for extremely large data sets.
Ensured application performance at scale without compromising accuracy.
Supported the rapid development of new features while maintaining system stability.
Our team migrated the system from MongoDB to SQL, optimized queries, and refactored the backend using microservices. With 100% unit test coverage, the platform now provides faster insights, continuous validation of security controls, and integration with vulnerability intelligence sources.