Thursday 26 September 2013

Big data school ( Hive,Map reduce,Hdfs) | Training

Big data school ( Hive,Map reduce,Hdfs) | Training
Course Contents
The course covers the following topics:
  • The Motivation For Hadoop

    • Problems with traditional large-scale systems
    • Requirements for a new approach

  • Hadoop: Basic Concepts

    • What is Hadoop?
    • The Hadoop Distributed File System
    • How MapReduce Works
    • Anatomy of a Hadoop Cluster

  • Writing a MapReduce Program

    • Examining a Sample MapReduce Program
    • Basic API Concepts
    • The Driver Code
    • The Mapper
    • The Reducer
    • Hadoop's Streaming API

  • The Hadoop Ecosystem

    • Hive and Pig
    • HBase
    • Flume
    • Other Ecosystem Projects

  • Integrating Hadoop Into The Workflow

    • Relational Database Management Systems
    • Storage Systems
    • Importing Data from RDBMSs With Sqoop
    • Importing Real-Time Data with Flume

  • Delving Deeper Into The Hadoop API

    • Using Combiners
    • The configure and close Methods
    • SequenceFiles
    • Partitioners
    • Counters
    • Directly Accessing HDFS
    • ToolRunner
    • Using The Distributed Cache

  • Common MapReduce Algorithms

    • Sorting and Searching
    • Indexing
    • Classification/Machine Learning
    • Term Frequency - Inverse Document Frequency
    • Word Co-Occurrence

  • Using Hive and Pig

    • Hive Basics
    • Pig Basics

  • Debugging MapReduce Programs

    • Testing with MRUnit
    • Logging
    • Other Debugging Strategies

  • Advanced MapReduce Programming

    • A Recap of the MapReduce Flow
    • Custom Writables and WritableComparables
    • The Secondary Sort
    • Creating InputFormats and OutputFormats
    • Pipelining Jobs With Oozie.

  • Joining Data Sets in MapReduce Jobs

    • Map-Side Joins
    • Reduce-Side Joins

  • Graph Manipulation in Hadoop

    • Introduction to graph techniques
    • Representing Graphs in Hadoop
    • Implementing a sample algorithm: Single Source Shortest Path.

    • or full course details please visit our website www.hadooponlinetraining.net

    • Duration for course is 30 days or 45 hours and special care will be taken. It is a one to one training with hands on experience.

    • * Resume preparation and Interview assistance will be provided.
    • For any further details please 

    • contact India +91-9052666559
    •          Usa : +1-678-693-3475.

    • visit www.hadooponlinetraining.net

    • please mail us all queries to info@magnifictraining.com

Sunday 22 September 2013

Big data training and placement | Big data School

Training Agenda:

Introduction to Apache Hadoop 
- Why are we here? What’s changed today? 
- What is Hadoop? 
- Hadoop architecture 

Hadoop Distributed File System (HDFS) 
- Storing and retrieving data from HDFS 
- HDFS operations 

Developing with MapReduce 
- How MapReduce works 
- Writing Mappers 
- Writing Reducers 
- Using Combiners for efficiency 

Modern Development Practices 
- Running with LocalJobRunner 
- Running within an IDE 
- Writing Unit Tests for Hadoop code 

Hadoop Projects: Hive 
- Data warehouses: then and now 
- Hive query language 
- Hive in practice 

Hadoop Projects: Pig 
- Pig: a DSL for MapReduce 
- Pig in practice 

Hadoop Projects: HBASE 
- Column-oriented database, real-time read-write access 
- HBASE in practice 

Wrap-up 
- Further resource 
- What’s new and exciting 
- See more at: 

or full course details please visit our website www.hadooponlinetraining.net

Duration for course is 30 days or 45 hours and special care will be taken. It is a one to one training with hands on experience.

* Resume preparation and Interview assistance will be provided.
For any further details please 

contact India +91-9052666559
         Usa : +1-678-693-3475.

visit www.hadooponlinetraining.net


please mail us all queries to info@magnifictraining.com