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Learn Hadoop and BigData with real examples and architecture of Hadoop

What you'll learn:

  • Understand what is BigData and Hadoop
  • What is HDFS and Map Reduce
  • You get practical knowledge how HDFS and Map Reduce Examples
  • Understand what the job of a Hadoop Developer /Tester looks like
  • This course is meant for students willing to next generation of IT
  • Understanding V's of Big Data
  • Evolution of Hadoop

Large data collections that can't be processed using conventional data processing technologies are what are known as big data or Hadoop.

These data sets are made up of data from various sources and are processed and analysed to get useful information.

Large e-commerce companies employ big data to compile data on user preferences and browsing habits in order to offer relevant suggestions and search results to specific users.

Course curriculum
  • Content of the Hadoop Course Big Data and Hadoop

    Introduction to Hadoop and big data
    Architectur Hadoop
    Java 1.8 and Ubuntu installation on VM Workstation 11
    Versioning and configuration of Hadoop
    only one node Installation of Hadoop 1.2.1 on a multi-node Ubuntu 14.4.1 Installation of Hadoop 1.2.1 using Linux and Hadoop commands on Ubuntu 14.4.1
    Block positioning and cluster architecture
    Hadoop Local Mode Modes
    Distributed Pseudo-Mode
    Complete Distribution Mode
    Master Daemons for Hadoop (Name Node, Secondary Name Node, Job Tracker)
    Master Daemons (Job tracker, Task tracker)
    Task Example
    HDFS Commands in Hadoop
    Accessing HDFS Using CLI
    Java Method

    Understanding Map-Reduce Streamline Framework Example to Word Count
    Utilizing Eclipse Luna HDFS to create a Map-Reduce programme. Method using the Read-Write Process Map-Reduce Life Cycle
    Serialization(Java) \sDatatypes
    the comparative and (Java)
    Using a custom output file to analyse a temperature dataset Map -Reduce
    Partitioner & Combiner Custom
    Using the local and pseudo-distributed modes of Map-Reduce.
    Java Enum (Advanced Map-Reduce) Custom and Dynamic Counters
    Multi-node MapReduce execution Hashtable Cluster
    Distribution of Custom Writable Site Data Using Configuration, DistributedCache, and Stringifie Input Formatters
    Input Formatter for NLine
    Sorting Reverse Sorting Secondary Sorting Compression Technique XML Input Formatter
    Use of the Sequence File Format
    Implementing the AVRO File Format
    Map testing
    MR Unit: Reduce
    Utilizing NYSE DataSets
    Utilizing Map-Reduce in Cloudera Box while Working with Million Song DataSets


    Installation & Introduction of the Hive
    Hive Commands Exploring Internal and External Table Partitions Data Types in Hive
    forms of complex data
    UDF in Hive Integrated UDF
    Java to Hive Connection Joins in Hive Working with HWI Bucket Map-side Custom UDF Thrift Server More commands to join
    Running Hive with SortBy Distribute By Lateral View in Cloudera

    Basics and Installations of Sqoop
    Advance Imports for Data Import from Oracle to HDFS
    Running Sqoop in Cloudera in Real-Time UseCase Exporting Data from HDFS to Oracle

    Installation and Introduction of the PIG
    Working With Complex Datatypes: WordCount in Pig NYSE in Pig
    PIG Schema
    Distinctive Command Group Filter Order Join Flatten Co-group Union Illustrate Miscellaneous
    Describe UDFs in Dry and Pig Parameter Substitution
    Run Pig Macros Using Cloudera for Pig

    Setting up Oozie
    Oozie running Map-Reduce
    Oozie is running Pig and Sqoop.

Big Data Hadoop FAQ’s:
1.What are the 4 components of Hadoop?

Hadoop is made up of four main components: HDFS, MapReduce, YARN, and Hadoop Common.

2.What is Hadoop and its components.

Apache Hadoop developed as a solution to "Big Data" as an issue. The framework Apache Hadoop offers us a number of tools or services to store and process Big Data. Big Data analysis and business decision-making are aided, which cannot be done effectively and efficiently with conventional methods.

3.What are the different data types in Pig Latin?

Pig Latin can handle both simple data types like tuples, bags, and maps as well as complicated data types like int, float, long, and double. Atomic or scalar data types are the fundamental data types used in all programming languages. Examples include string, int, float, long, double, char[], and byte[]. Tuple, Map, and Bag are examples of complex data types.

4.How do you configure an “Oozie” job in Hadoop?

As part of the Hadoop stack, "Oozie" is integrated and supports a variety of Hadoop tasks, including "Java MapReduce," "Streaming MapReduce," "Pig," "Hive," and "Sqoop."

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