An Empirical Analysis of Big Data Frameworks for Data Management

Abstract

The high proliferation of digital technologies has resulted in the creation of large size of data in different sources including social media and sensors, business transactions and online platforms. The current data management systems will not be adequate to manage these vast, complicated and rapidly expanding data. The Big Data architectures are the options that can serve to store, process, and handle the large-sized data efficiently. The paper provides empirical examination of the popular Big Data frameworks applied to managing data such as Hadoop, Apache Spark, and Apache Flink. The paper contrasts these architecture models with regard to performance, scalability, fault tolerance, processing speed and usability. Results of this study assist companies and scholars to select appropriate big data systems based on their data management needs.