Understand the working of Sqoop and hive in Hadoop

Abstract

In past decades, the structured and consistent data analysis has seen huge success. It is a challenging task to analyse the multimedia data which is in unstructured format. Here the big data defines the huge volume of data that can be processed in distributed format. The big data can be analysed by using the hadoop tool which contains the Hadoop Distributed File System (HDFS) storage space and inbuilt several components are there. Hadoop manages the distributed data which is placed in the form of cluster analysis of data itself. In this, it shows the working of Sqoop and Hive in hadoop. Sqoop (SQL-to-Hadoop) is one of the Hadoop component that is designed to efficiently imports the huge data from traditional database to HDFS and vice versa. Hive is an open source software for managing large data files that is stored in HDFS. To show the working, here we are taking the application Instagram which is a most popular social media. In this analyze the data that is generated from Instagram that can be mined and utilized by using Sqoop and Hive. By this, prove that sqoop and hive can give results efficiently. This paper gives the details of sqoop and hive working in hadoop.

Analysis and Research on the Construction of College Spirit under Big Data Technology

“…As for the software program, Linux is selected as the bottom operating system of each server, the version is CentOS 7.5, jdk1.8.0_171 is selected as the JDK version, and Hadoop is selected as v2.6.0, and components such as Spark, Yarn, HDFS, Zookeeper, HBase, Kafka and Redis are also deployed in each node. [5] In addition, the WebCollector-Hadoop version is selected for the WebCollector crawler framework, and it is directly deployed on the Hadoop framework to realize the distributed collection of online public opinion data information. The text processing module completes Jieba word segmentation and TF-IDF feature operation under the Spark framework.…”