Scalable Architectures for Big Data Processing and Reporting

The rapid expansion of data generation has created a critical demand for scalable architectures capable of efficiently processing and reporting massive datasets. Industries worldwide depend on robust systems to extract actionable insights from big data, ensuring speed, reliability, and adaptability. This research delves into scalable frameworks for big data processing and reporting, emphasizing performance optimization, seamless integration, and meeting diverse organizational needs.

Distributed computing frameworks such as Apache Hadoop and Apache Spark are the foundation of scalable big data architectures. These systems leverage clusters of machines to process extensive datasets through parallel execution and distributed computation. This study evaluates these frameworks’ scalability, dynamic resource allocation, and effectiveness in managing varying workload intensities.