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    1. Big Data
      • What is big data?
      • Challenges in big data
      • Challenges in traditional Applications
      • New Requirement
      • Introducing hadoop development training
      • Brief History of hadoop development training
      • Features of Hadoop
      • Overview of hadoop development training Ecosystem
      • Overview of MapReduce
    2. Hadoop Administration
        Linux –
      • Basic architecture
      • Important commands
      • File permission and ownership
      • Administration
      • Communication
      • Pipe etc.
      • Setup Single (pseudo-node) Cluster
      • Important Directories,
      • Configuring HDFS & Important Configuration Properties.
      • Interacting with HDFS.
      • Common Example Operations
      • HDFS Command Reference
      • DFSAdmin Command Reference
      • Using HDFS For MapReduce
      • HDFS Web Interface
      • And how to Setup Multi-node Cluster.
      • Hands–on Exercises and Assignment.
      • Additional HDFS Tasks
      • Rebalancing Blocks
      • Copying Large Sets of Files
      • Decommissioning Nodes
      • Verifying File System Health
      • Rack Awareness
      • Cluster Configuration
      • Small Clusters: 2-10 Nodes
      • Medium Clusters: 10-40 Nodes
      • Large Clusters: Multiple Racks
      • Performance Monitoring
      • Ganglia
      • Nagios
      • Hands-on Exercises and Assignment
    3. Hadoop Development
<ol> <li>Big Data</li> <ul> <li>What is big data?</li> <li>Challenges in big data</li> <li>Challenges in traditional Applications</li> <li>New Requirement</li> <li>Introducing hadoop development training</li> <li>Brief History of hadoop development training</li> <li>Features of Hadoop</li> <li>Overview of hadoop development training Ecosystem</li> <li>Overview of MapReduce</li> </ul> <li>Hadoop Administration</li> <ul> Linux – <li>Basic architecture</li> <li>Important commands</li> <li>File permission and ownership</li> <li>Administration</li> <li>Communication</li> <li>Pipe etc. </li> </ul> <ul> <li>Setup Single (pseudo-node) Cluster</li> <li>Important Directories, </li> <li>Configuring HDFS & Important Configuration Properties. </li> <li> Interacting with HDFS.</li> <li>Common Example Operations</li> <li>HDFS Command Reference</li> <li>DFSAdmin Command Reference</li> <li>Using HDFS For MapReduce</li> <li>HDFS Web Interface</li> <li>And how to Setup Multi-node Cluster. </li> <li>Hands–on Exercises and Assignment. </li> </ul> <ul> <li>Additional HDFS Tasks</li> <li>Rebalancing Blocks</li> <li>Copying Large Sets of Files</li> <li>Decommissioning Nodes</li> <li>Verifying File System Health</li> <li>Rack Awareness</li> <li>Cluster Configuration</li> <li>Small Clusters: 2-10 Nodes</li> <li>Medium Clusters: 10-40 Nodes</li> <li>Large Clusters: Multiple Racks</li> <li>Performance Monitoring</li> <li>Ganglia</li> <li>Nagios</li> <li>Hands-on Exercises and Assignment</li> </ul> <li> Hadoop Development</li>

Big Data and Hadoop Training

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Big Data and Hadoop Training

Bigdata and Hadoop development training in Marathahalli Bangalore Module 1 – Introduction to Hadoop and its Ecosystem, Map Reduce and …

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    Bigdata and Hadoop development training in Marathahalli Bangalore

    Module 1 – Introduction to Hadoop and its Ecosystem, Map Reduce and HDFS

    • Big Data, Factors constituting Big Data
    • Hadoop and Hadoop Ecosystem
    • Map Reduce -Concepts of Map, Reduce, Ordering, Concurrency, Shuffle, Reducing, Concurrency
    • Hadoop Distributed File System (HDFS) Concepts and its Importance
    • Deep Dive in Map Reduce – Execution Framework, Partitioner, Combiner, Data Types, Key pairs
    • HDFS Deep Dive – Architecture, Data Replication, Name Node, Data Node, Data Flow
    • Parallel Copying with DISTCP, Hadoop Archives

    Hadoop development training Course Content

    A) Big Data – Motivation & Basics.

    B) Hadoop Administration – Architecture, Setups, Manipulation & Maintenances ……… (with Prerequisites -Linux).

    C) Hadoop Development – a) MapReduce (Basics) b)Real World MapReduce (Advance) …..(with Prerequisites –Java).

    D) Corporate Technologies – Hive, Pig, HBase, Oozie, Flume, Sqoop, Zookeeper, Mahout ……(with Prerequisites –SQL).

    E) Cloud Computing – Concepts & Deploying hadoop on cloud (AWS- EC2, S3, EMR, & others as per requirement of Project).

    F) Beyond Hadoop – Strom, Sparks, Mesos…..etc & future scope of Hadoop with these coming technologies.

    ——————&md
    ash;-

    A) Big Data

    What is big data?
    Challenges in big data
    Challenges in traditional Applications
    New Requirement
    Introducing hadoop development training
    Brief History of hadoop development training
    Features of Hadoop
    Overview of hadoop development training Ecosystem
    Overview of MapReduce
    B) Hadoop Administration

    1) Linux –

    Basic architecture
    Important commands
    File permission and ownership
    Administration
    Communication
    Pipe etc.
    2) Setup Single (pseudo-node) Cluster

    Important Directories,
    Configuring HDFS & Important Configuration Properties.
    3) Interacting with HDFS.

    Common Example Operations
    HDFS Command Reference
    DFSAdmin Command Reference
    Using HDFS For MapReduce
    HDFS Web Interface
    And how to Setup Multi-node Cluster.
    Hands–on Exercises and Assignment.
    4) Additional HDFS Tasks

    Rebalancing Blocks
    Copying Large Sets of Files
    Decommissioning Nodes
    Verifying File System Health
    Rack Awareness
    Cluster Configuration
    Small Clusters: 2-10 Nodes
    Medium Clusters: 10-40 Nodes
    Large Clusters: Multiple Racks
    Performance Monitoring
    Ganglia
    Nagios
    Hands-on Exercises and Assignment
    C) Hadoop Development

    1) MapReduce -1

    Java – basic Oops concepts, Serialization, I/O, Collection, Sorts ..etc.
    Configure eclipse environment for Mapreduce development & run first Program.
    Hands-on Exercises and Assignment.
    2) MapReduce -2

    Explanation of first program in details describing Mapper, Reducer, Driver.
    MapReduce Algorithms and whole process flow – map, partition, sort, shuffle, reduce.
    Related terms – Input formats, Input Splits, Speculative Execution..etc
    Other related Algorithm – Combiner, Partitioner.
    Hands-on Exercises and Assignment.
    3) MapReduce -3

    Discussion and solution of various program and their use cases in real world.
    Local Runner and Usage of Tool runners.
    Setup/Cleanup method in mapper/reducer.
    Passing the parameters to mapper and reducer.
    Searching Algorithm.
    Distributed cache.
    Hands-on Exercises and Assignment.
    4) Real World MapReduce -1 (Advance)

    Create custom keys and values.
    Create custom partitioner.
    Write custom input format.
    Hands-on Exercises and Assignment.
    5) Real World MapReduce -2

    Implementing Custom comparator.
    Secondary sorting.
    Relational Manipulation– map-side,reduce-side joins.
    Real-World Data mining.
    Hands-on exercises and Assignment.
    D) Corporate Technologies

    1) SQL, HBase & other Components.

    Introduction to Sqoop, Hive, Pig, Oozie, Flume, Mahout… their use cases and installation.
    Introduction to HBase, Architecture, Map Reduce Integration, Different Client API –Feature and Administration.
    Hands-on exercises and Assignment.
    2) Hive

    Understanding Hive, Architecture, Physical Model, DataTypes.
    Hive QL –DDL, DML, Other Operations.
    Understanding Tables in Hive, Partitioning, Indexes, Bucketing, Joining Tables, Data Load…etc
    Hands-on Exercises and Assignment.
    3) Pig

    Understanding Pig, Different Mode and Data Model
    Advance Pig Latin, Evaluation and Filter Functions..etc.
    Real time use cases.
    When to use pig and when to use hive.
    Hands-on Exercises and Assignment.
    4) Cloud Computing on hadoop development training.

    Introduction, Options and how to use.
    AWS(Amazon Web Services)- Registration and AMI setup.
    Create multimode cluster using S3, EC2..and run MapReduce, pig and hive program.
    F) Beyond Hadoop

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    Certification

    This course is designed for clearing Cloudera Certified Developer for Apache Hadoop (CCDH).

    Self Paced vs Instructor LED Online

    Self-Paced Courses

    • Students learn via video tutorials which can be played multiple times
    • This is a self –learning course and Learners choose their own study time
    • 24*7 support for queries and doubts clearance over email.  Session with a trainer can be arranged if required
    • Very affordable. 75% cheaper than online instructor-led courses
    • Lifetime access to video tutorials with free upgrade to latest topics

    Online Training – Instructor-Led

    • Students learn in a virtual classroom from a trainer
    • Course follows a set time table and duration where Learners can log in at the allocated time only
    • Queries addressed only in the live session
    • Costlier than self-paced
    TAKE ONLINE/CLASS ROOM TRAININGRs. 14,800.00 Rs. 9,000.00TAKE SELF-PLACED TRAININGRs. 10,000.00 Rs. 6,160.00

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