Designing Microservices That Handle High-Volume Data Loads
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V4I4P109Keywords:
Microservices, High-Volume Data, Event-Driven Architecture, Data Streaming, Scalability, Resilience, Kafka, Load Balancing, Data Ingestion, Real-Time Processing, Asynchronous Communication, System DesignAbstract
If microservices are to govern meaningful volume data flows, they must be precisely balanced in scalability, performance, and durability. As companies depend more on data-driven systems, microservices must be developed not only for usefulness but also for their capacity to effectively analyze, move, and react to large data quantities. The main difficulty is letting horizontal scaling of these services while preserving data integrity and reducing latency. Among architectural solutions, asynchronous communication, event-driven patterns, and reactive design concepts will help to relieve traffic and preserve responsiveness in great demand. By use of technologies such as message queues, streaming platforms, and non-blocking APIs, microservices can maintain loose coupling and react to real-time needs. Where milliseconds count real-time data processingeffective memory management, control of schema evolution, and thorough monitoring systems also demand careful attention. This abstract shows how a company effectively turned their historical monolith into high-throughput microservices using Kafka, Kubernetes, and event sourcing to run millions of daily transactions constantly. The occasion highlights solid knowledge of decoupling logic, independent component scaling, and backpressure mechanism utilization to ensure service stability. Designing microservices for high-volume data loads finally requires not only for picking appropriate technology but also for building a flexible, visible ecosystem whereby resilience and performance are mutually dependent. Resources here help architects and builders striving to ensure the longevity of their systems in a more real-time, data-driven environment
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