Apache Hadoop and Mapreduce Interview Questions and Answers

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz Language: English | Size: 576 MB | Duration: 1.5 hours What you'll learn By attending this course you will get to know frequently and most likely asked Programming, Scenario based, Fundamentals, and Performance Tuning based Question asked in Apache Hadoop and Mapreduce Interview along with the answer This will help Bigdata Career Aspirants to prepare for the interview. During your Scheduled Interview you do not have to spend time searching the Internet for Apache Hadoop and Mapreduce Interview questions. We have already compiled the most frequently asked and latest Apache Hadoop and Mapreduce Interview questions in this course. Requirements Apache Hadoop and Mapreduce basic fundamental knowledge is required Description Apache Hadoop and Mapreduce Interview Questions has a collection of 120+ questions with answers asked in the interview for freshers and experienced (Programming, Scenario-Based, Fundamentals, Performance Tuning based Question and Answer). This course is intended to help Apache Hadoop and Mapreduce Career Aspirants to prepare for the interview. We are planning to add more questions in upcoming versions of this course. The Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. Hadoop MapReduce is a software framework for easily writing applications which process vast amounts of data (multi-terabyte data-sets) in-parallel on large clusters (thousands of nodes) of commodity hardware in a reliable, fault-tolerant manner. A MapReduce job usually splits the input data-set into independent chunks which are processed by the map tasks in a completely parallel manner. The framework sorts the outputs of the maps, which are then input to the reduce tasks. Typically both the input and the output of the job are stored in a file-system. The framework takes care of scheduling tasks, monitoring them and re-executes the failed tasks. Typically the compute nodes and the storage nodes are the same, that is, the MapReduce framework and the Hadoop Distributed File System (see HDFS Architecture Guide) are running on the same set of nodes. This configuration allows the framework to effectively schedule tasks on the nodes where data is already present, resulting in very high aggregate bandwidth across the cluster. Course Consist of the Interview Question on the following Topics Single Node Setup Cluster Setup Commands Reference FileSystem Shell Compatibility Specification Interface Classification FileSystem Specification Common CLI Mini Cluster Native Libraries HDFS Architecture Commands Reference NameNode HA With QJM NameNode HA With NFS Federation ViewFs Snapshots Edits Viewer Image Viewer Permissions and HDFS Quotas and HDFS Disk Balancer Upgrade Domain DataNode Admin Router Federation Provided Storage MapReduce Distributed Cache Deploy Support for YARN Shared Cache MapReduce REST APIs MR Application Master MR History Server YARN Architecture Commands Reference ResourceManager Restart ResourceManager HA Node Labels Node Attributes Web Application Proxy Timeline Server Timeline Service V.2 Writing YARN Applications YARN Application Security NodeManager Using CGroups YARN Federation Shared Cache YARN UI2 YARN REST APIs Introduction Resource Manager Node Manager Timeline Server Timeline Service V.2 YARN Service Yarn Service API Hadoop Streaming Hadoop Archives Hadoop Archive Logs DistCp Hadoop Benchmarking Reference Changelog and Release Notes Configuration core-default.xml hdfs-default.xml hdfs-rbf-default.xml mapred-default.xml yarn-default.xml Deprecated Properties Who this course is for: This course is designed for Apache Hadoop and Mapreduce Job seeker with 6 months to 2 years of Experience in Apache Hadoop and Mapreduce or Big data Hadoop Development and looking out for new job as Developer,Bigdata Engineers or Developers, Software Developer, Software Architect, Development Manager Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me

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