BIG DATA HADOOP TARINING | TRAININGS24x7

BIG DATA HADOOP CERTIFICATION TARINING

Course Overview 
This big data course is designed to enhance your skill and knowledge to become a successful Hadoop developer. Core concepts will be covered in the course along with implementation on varied industry cases. This big data course is designed to enhance your skill and knowledge to become a successful Hadoop developer. Core concepts will be covered in the course along with implementation on varied industry cases.

Training Objectives:
Course Objectives
By the end of the course,  you will:
1. Master the concepts of HDFS and MapReduce framework
2. Understand Hadoop 2.x Architecture
3. Setup Hadoop Cluster and write Complex MapReduce programs
4. Learn data loading techniques using Sqoop and Flume
5. Perform data analytics using Pig, Hive and YARN
6. Implement HBase and MapReduce integration
7. Implement Advanced Usage and Indexing
8. Schedule jobs using Oozie.
9. Implement best practices for Hadoop development
10. Work on a real life Project on Big Data Analytics
Course Curriculum of Big data and Hadoop

  1. Understanding Big data and Hadoop  
    .        Understanding of Big data.
    ·        Limitations and Solutions of existing Data Analytics Architecture.
    ·        Hadoop feature and Hadoop Ecosystem.
    ·        Hadoop 2.x core Components.
    ·        Hadoop storage: HDFS and Hadoop Processing: MapReduce.
    ·        Anatomy of File write and Read and Rack Awareness.

    2.    Hadoop Architecture and HDFS 
    ·        HadoopInstallation and Configuration in system.
    ·        Common Hadoop Shell Commands.
    ·        Hadoop Configuration file.
    ·        Password-Less SSH.
    ·        Hadoop copy Commands.

    3.    HadoopMapReduce Framework  |
    ·        MapReduce Use Cases.
    ·        Traditional way VsMapReduce way.
    ·        Hadoop 2.x MapReduce Architecture and components
    ·        Demo on MapReduce.
    ·        Input Splits.
    ·        Relation between InputSplit and HDFS Blocks.
    ·        MapReduce job submission Flow.
    ·         MapReduce: Combiner and Partitioner..
    ·         Map-Side Joins
    ·        Reduce-Side Joins

    4.    Pig 
    ·        Pig Installation and Configuration.
    ·        About Pig.
    ·        MapReduceVs Pig.
    ·        Use Cases.
    ·        Programming structure in Pig.
    ·        Pig Running Modes.
    ·        Pig Components and Pig execution.
    ·        Pig Latin Program.
    ·        Data Models and Data types in Pig.
    ·        Relations Operators and File Loaders.
    ·        Group and COGROUP Operators.
    ·        Joins and COGROUP in Pig.
    ·        Union.
    ·        Diagnostic Operators..

    5.     Hive        
    ·        Hive Installation and Configuration.
    ·        About Hive and Use Cases
    ·        Hive Vs Pig
    ·        Hive Architecture and Components.
    ·        MetaStore in Hive and Limitations of Hive.
    ·        Comparison with Traditional Databases.
    ·        Hive data types and data models.
    ·        Partitions and Buckets.
    ·        Hive Tables (Managed Tables and External Tables).
    ·        Importing Data.
    ·        Querying Data.
    ·        Hive scripts.
    ·        Hive QL: Joining Tables.
    ·        Hive dynamic partitioning.
    ·        Buckting

    6.    Hbase    
    ·       Introduction to NoSQL database and Hbase.
    ·       Hbase Installation and Configuration.
    ·       HbaseVs RDBMS.
    ·       Hbase Components and Architecture.
    ·       Hbase Data Models and Shell.
    ·       Data Loading Techniques and Filter in Hbase.
    ·       Introduction to Zookeeper and Data Model.
    ·        Zookeeper Service.
    ·       Hands on Bulk data loading.
    ·       Introduction on Oozie.

    7.       Data Loading Technique using Sqoop.
    ·        Load table into MySQL
    ·        Import MySQL to HDFS
    ·        Export Table HDFS to MySQL
    ·        Import part of table MYSQL to HDFS
    ·        Import selected Data From Table to HDFS
    ·        Import Data HDFS to Hive
    ·        Cloudera data to Windows
    ·        Windows Data to Cloudera
    ·        Local System data to cloudera

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