Spark And Hive In Big Data Analysis Assignment

Assignment Task

Introduction

It is currently important to analyze Big data properly for the benefit of a business or for research because the volume of data is growing at an exponential rate. In order to analyze the massive amounts of data and provide useful results from it, query processing is a crucial step. Spark, which includes RDD, and Hive, which is MapReduce-based, are the two main technologies utilized for this query processing. Facebook built a data warehouse called Hive where SQL queries can be run. Internally, these queries are transformed into MapReduce tasks, which are subsequently executed on HDFS and Hadoop. Spark is a platform for processing data in real time. It is an open-source framework that uses resilient distributed datasets and SparkSQL modes for rapid queries on structured data (RDDs). As these query technologies advance, cloud platforms will simply offer these services on-demand, eliminating the need for installation and configuration work and increasing the focus on actual data analysis and query capabilities. Two of these cloud-based Hadoop solutions are ClouderaCDH and AWS EMR.

Research Question

1) Which is the most appropriate query tool for big data analysis in the Hadoop ecosystem between Hive and SparkSQL?

2) Does the query processing performance of SparkSQL and Hive depend on the Hadoop cloud platform?

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