Hadoop YARN | Hadoop YARN Architecture | Hadoop YARN Tutorial | Hadoop Tutorial | Simplilearn

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    This Hadoop YARN tutorial will help you understand the Hadoop 1.0 and Hadoop 2.0, limitations of Hadoop 1.0, need for YARN, what is YARN, workloads running on YARN, YARN components, YARN architecture and you will also go through a demo on YARN. YARN is the cluster resource management layer of the Apache Hadoop Ecosystem, which schedules jobs and assigns resources. Hadoop 1.0 is designed to run MapReduce jobs only and had issues in scalability, resource utilization, etc. whereas YARN solved those issues and users could work on multiple processing models. Now let us get started and learn YARN in detail.

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    Below topics are explained in this Hadoop YARN tutorial:
    1. Hadoop 1.0 (MapReduce 1) (00:34)
    2. Limitations of Hadoop 1.0 (MapReduce 1) (05:09)
    3. Need for YARN (07:21)
    4. What is YARN (10:11)
    5. Workloads running on YARN (11:29)
    6. YARN components (12:16)
    7. YARN architecture (30:26)
    8. Demo on YARN (32:37)

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    #HadoopYarn #YarnTutorial #HadoopYarnArchitecture #HadoopYarnBasics #HadoopYarnExample #LearnHadoop #HadoopTraining #HadoopCertification #SimplilearnHadoop #Simplilearn

    Simplilearn’s Big Data Hadoop training course lets you master the concepts of the Hadoop framework and prepares you for Cloudera’s CCA175 Big data certification. With our online Hadoop training, you’ll learn how the components of the Hadoop ecosystem, such as Hadoop 3.4, Yarn, MapReduce, HDFS, Pig, Impala, HBase, Flume, Apache Spark, etc. fit in with the Big Data processing lifecycle. Implement real life projects in banking, telecommunication, social media, insurance, and e-commerce on CloudLab.

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    The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.

    What are the course objectives?
    This course will enable you to:
    1. Understand the different components of Hadoop ecosystem such as Hadoop 2.7, Yarn, MapReduce, Pig, Hive, Impala, HBase, Sqoop, Flume, and Apache Spark
    2. Understand Hadoop Distributed File System (HDFS) and YARN as well as their architecture, and learn how to work with them for storage and resource management
    3. Understand MapReduce and its characteristics, and assimilate some advanced MapReduce concepts
    4. Get an overview of Sqoop and Flume and describe how to ingest data using them
    5. Create database and tables in Hive and Impala, understand HBase, and use Hive and Impala for partitioning
    6. Understand different types of file formats, Avro Schema, using Arvo with Hive, and Sqoop and Schema evolution
    7. Understand Flume, Flume architecture, sources, flume sinks, channels, and flume configurations
    8. Understand HBase, its architecture, data storage, and working with HBase. You will also understand the difference between HBase and RDBMS
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    Big Data career opportunities are on the rise, and Hadoop is quickly becoming a must-know technology for the following professionals:
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