Apache Spark Big data Analysis, Performance Tuning, and Spark Application Optimization

Big data analysis, the processing of vast and diverse data, is crucial for enhancing business intelligence, predictive modeling, and quicker decision-making. It encompasses structured, unstructured, and semi-structured data, ranging from TBs to ZBs. Popular open-source tools like Apache Hadoop and Apache Spark are instrumental for managing this data deluge. Apache Spark is described as a superior environment in this article for handling huge data carefully and effectively. A few of the studies, technical achievements, and popular future development paths of Big Data Analysis utilizing Apache Spark are highlighted. Apache Spark can offer a better working environment than Apache Hadoop, however it cannot be argued that it is not widespread in Industry 4.0 such that it could outreach the Hadoop. The main factor is Hadoop's use of the simple, comprehensible, and straightforward MapReduce programming model. However, the users of Spark platform are well-pleased with the versatile processing capabilities. As Spark is mainly based on Resilient Distributed Data-sets and RDDs are outmoded now, there is a severe necessity of performance tuning that ensures flawless working from Spark and Spark based APIs. It is a need of the hour scenario where Spark applications should adopt advanced optimization techniques.