what is hadoop used for - Wikitechy https://www.wikitechy.com/interview-questions/tag/what-is-hadoop-used-for/ Interview Questions Wed, 22 Sep 2021 05:53:05 +0000 en-US hourly 1 https://wordpress.org/?v=6.9.4 https://www.wikitechy.com/interview-questions/wp-content/uploads/2025/10/cropped-wikitechy-icon-32x32.png what is hadoop used for - Wikitechy https://www.wikitechy.com/interview-questions/tag/what-is-hadoop-used-for/ 32 32 What is the difference between Apache Hadoop and Cloudera in big data ? https://www.wikitechy.com/interview-questions/cloudera-impala/what-is-the-difference-between-apache-hadoop-and-cloudera-in-big-data/ https://www.wikitechy.com/interview-questions/cloudera-impala/what-is-the-difference-between-apache-hadoop-and-cloudera-in-big-data/#respond Mon, 19 Jul 2021 06:38:24 +0000 https://www.wikitechy.com/interview-questions/?p=1023

Difference between Apache Hadoop and Cloudera in big data

  • Apache Hadoop is the Hadoop distribution from Apache group.
  • Cloudera Hadoop has its own supply of Hadoop which is designed on top of Apache Hadoop. so it does not have latest release of Hadoop.
  • Cloudera Hadoop contains extra tools. while Hadoop distributions like Cloudera Search, Impala, Cloudera Navigator and Cloudera Manager these are not involved in Cloudera Hadoop.
  • These additional tools turns out Cloudera Hadoop to be slightly open source and proprietary.
Distribution hadoop
  • Apache Hadoop is very important to use to cope up with any data, no matter how large or what type or complex it is. It works like a fluid way or can say it is very faster, cheaper and reliable analytic tool than others.
  • Even, it can easily work on unstructured, and schemaless data and people can easily ground their data into a format without reformating or putting any other efforts.
  • Apart from this, via Apache Hadoop various programming model has been simplified to allow you to quickly write and test the software in distributed systems.
  • This is an open-source Apache Hadoop distribution usually, focus on enterprise-class deployments.
  • Cloudera Inc is a very popular American software company, known for providing Apache Hadoop-based software, services, and full support for the same.
  • With Cloudera, it is said that Hadoop is just a starting to create your data management strategy, and you can easily use the same to add the security and other various functions to create an enterprise-grade foundation for your data.
]]> https://www.wikitechy.com/interview-questions/cloudera-impala/what-is-the-difference-between-apache-hadoop-and-cloudera-in-big-data/feed/ 0 What is the use of hive language ? https://www.wikitechy.com/interview-questions/hive/what-is-the-use-of-hive-language/ https://www.wikitechy.com/interview-questions/hive/what-is-the-use-of-hive-language/#respond Tue, 13 Jul 2021 21:20:39 +0000 https://www.wikitechy.com/interview-questions/?p=564

Uses of Hive language

  • Hive is often used as the interface to an Apache Hadoop based data warehouse.
  • Hive is considered friendlier and more familiar to users who are used to using SQL for querying data.
  • It is a platform to develop SQL scripts to perform MapReduce operations.
why is hive language used for
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Why do we need Data Locality in Hadoop ?

Data Locality in Hadoop
  • Datasets in HDFS store as blocks in DataNodes the Hadoop cluster.
  • During the execution of a MapReduce job the individual Mapper processes the blocks (Input Splits).
  • If the data does not reside in the same node where the Mapper is executing the job, the data needs to be copied from the DataNode over the network to the mapper DataNode.
Datasets in HDFS - Data Locality in Hadoop
  • Now if a MapReduce job has more than 100 Mapper and each Mapper tries to copy the data from other DataNode in the cluster simultaneously, it would cause serious network congestion which is a big performance issue of the overall system.
  • Hence, data proximity to the computation is an effective and cost-effective solution which is technically termed as Data locality in Hadoop. It helps to increase the overall throughput of the system.

Types of data locality

  • Data local
    • In this type data and the mapper resides on the same node. This is the closest proximity of data and the most preferred scenario.
  • Rack Local
    • In this type data and the mapper resides on the same node. This is the closest proximity of data and the most preferred scenario.
    • In this scenarios mapper and data reside on the same rack but on the different data nodes.
  • Different Rack
    • In this scenario mapper and data reside on the different racks.
Types of data locality
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Why Hadoop used for Big Data Analytics ?

  • Big data analytics is the process of examining large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences and other useful business information.
  • Hadoop is a framework to store and process big data. Hadoop specifically designed to provide distributed storage and parallel data processing that big data requires.

Hadoop is the best solution for storing and processing big data because:

Hadoop stores huge files as they are (raw) without specifying any schema.

  • High scalability – We can add any number of nodes, hence enhancing performance dramatically.
  • High availability – In hadoop data is highly available despite hardware failure. If a machine or few hardware crashes, then we can access data from another path.
  • Reliable – Data is reliably stored on the cluster despite of machine failure.
  • Economic – Hadoop runs on a cluster of commodity hardware which is not very expensive.

What is Hadoop ?

  • Hadoop is an open source project from Apache Software Foundation.
  • It provides a software framework for distributing and running applications on clusters of servers that is inspired by Google’s Map-Reduce programming model as well as its file system(GFS).
  • Hadoop was originally written for the nutch search engine project.
  • Hadoop is open source framework written in Java. It efficiently processes large volumes of data on a cluster of commodity hardware.
  • Hadoop can be setup on single machine , but the real power of Hadoop comes with a cluster of machines , it can be scaled from a single machine to thousands of nodes. Hadoop consists of two key parts,
    • Hadoop Distributes File System(HDFS)
    • Map-Reduce.
Hadoop Overview

Hadoop Distributed File System(HDFS)

  • HDFS is a highly fault tolerant, distributed, reliable, scalable file system for data storage.
  • HDFS stores multiple copies of data on different nodes; a file is split up into blocks (Default 64 MB) and stored across multiple machines.
  • Hadoop cluster typically has a single namenode and number of datanodes to form the HDFS cluster.

Map-Reduce

  • Map-Reduce is a programming model designed for processing large volumes of data in parallel by dividing the work into a set of independent tasks.
  • It is also a paradigm for distributed processing of large data set over a cluster of nodes.
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