Big Data Hadoop Course And Training

Big Data Hadoop Course And Training

The course duration in 36 hours and aspirants will be trained in latest Hadoop technologies which is industry standard. By the end of the course aspirants will be familiar with the basic understanding of Hadoop framework.

Big Data?

Big data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process using on-hand data management tools or traditional data processing applications.

The challenges include capture, curation, storage, search, sharing, transfer, analysis and visualization. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to spot business trends, prevent diseases, combat crime and so on.

 

What is Hadoop?

 

Hadoop is a framework which is designed to solve the problems related to Big Data. Each and everyday numerous amount of raw data is generated from different kinds of sources, this data contains lot of useful information which help solve many different kinds of problems. Hadoop helps in analysis of this huge data and provides with useful information.

Pre-Requisite:
  • To learn Hadoop one has to have basic knowledge of OOP concepts and programming using Core Java. SQL knowledge is also preferred

Goal of Hadoop program in Real Time Signals Technologies:

The goal of this program is to make the candidate a complete Big Data professional by imparting all the knowledge required to become a successful Hadoop Developer.

Job Responsibilities of aHadoop Developer

  • Analytical and problem solving skills, applied to a Big Data environment.

  • Deep understanding and related experience with Hadoop stack, HBase, Hive, Pig, Sqoop

  • Hands-on experience with related/complementary open source software platforms and programming (e.g. Java, Linux)

  • Good experience in writing map-reduce based algorithms and programs

  • Knowledge and hands-on experience with ETL (Extract-Transform-Load) tools (e.gSqoop, Flume)

  • Understanding of BI tools and reporting software and their capabilities (e.g. Business Objects)

  • Sound knowledge of No-SQL databases and Relational Databases (RDBMS) as well as SQL

  • Experience with agile/scrum methodologies to iterate quickly on product changes, developing user stories and working through backlogs

  • Should be very analytical with ability to understand and interpret the business data

TIMINGS:
  • We are providing classes during weekdays/ weekends (2-3 hrs)

  • Training given by Real time expert.

Learn By Examples – Pro Hadoop
Chapter 1: Why is Big Data a Big Deal?
  • Introduction

  • The Big Data Paradigm

  • Serial vs Distributed Computing

  • What is Hadoop?

  • HDFS or the Hadoop Distributed File System

  • MapReduce Introduced

  • YARN or Yet another Resource Negotiator

Chapter 2: Installing Hadoop in a Local Environment
  • Hadoop Install Modes

  • Setup a Virtual Linux Instance (For Windows users)

  • Hadoop Standalone mode Install

  • Hadoop Pseudo-Distributed mode Install

Chapter 3: The MapReduce "Hello World"
  • The basic philosophy underlying MapReduce

  • Reduce - Visualized and Explained Map

  • MapReduce - Digging a little deeper at every step

  • "Hello World" in MapReduce

  • The Mapper

  • The Reducer

  •  The Job

Chapter 4: Run a MapReduce Job

  • Get comfortable with HDFS

  • Run your first MapReduce Job

CH 5: More MapReduce - Combiners, Shuffle and Sort and Streaming API

  • Parallelize the reduce phase - use the

  • Not all Reducers are

  • How many mappers and reducers does your MapReduce have?

  • Parallelizing reduce using Shuffle and Sort

  • MapReduce is not limited to the Java language - Introducing the Streaming API

  • Python for MapReduce


CH 6: HDFS and Yarn

  • HDFS - Protecting against data loss using replication

  • HDFS - Name nodes and why they're critical

  • HDFS – Check pointing to backup name node information

  • Yarn - Basic components

  • Yarn - Submitting a job to Yarn

  • Yarn - Plug in scheduling policies

  • Yarn - Configure the scheduler

CH 7: Setting up a Hadoop Cluster

  • Manually configuring a Hadoop cluster (Linux VMs)

  • Getting started with Amazon Web Service’s

  • Start a Hadoop Cluster with Cloud era Manager on AWS

 

CH 8: MapReduce Customizations for Finer Grained Control

  • Setting up your MapReduce to accept command line arguments

  • The Tool, Tool Runner and GenericOptionsParser

  • Configuring properties of the Job

  • Customizing the Partitioner, Sort Comparator, and Group Comparator

CH 9: Inverted Index, Custom Data Types for Keys, Bigram Counts and UT
 The heart of search engines - The Inverted Index
 Generating the inverted index using MapReduce
 Custom data types for keys - The Writable
 Represent a Bigram using a
 MapReduce to count the Bigrams in input
 Test your MapReduce job

 

CH 10: Input and Output Formats and Customized Partitioning
 Introducing the File Input
 Text and Sequence File

 Data partitioning using a custom
 Make the custom partitioner real in
 Total Order Partitioning
 Input Sampling, Distribution, Partitioning and configuring
 Secondary Sort

 

CH 11: Recommendation Systems using Collaborative Filtering
 Introduction to Collaborative Filtering
 Friend recommendations using chained MR jobs
 Get common friends for every pair of users - the first MapReduce
 Top 10 friend recommendation for every user - the second MapReduce

 

CH 12: Hadoop as a Database
 Structured data in Hadoop
 Running an SQL Select with MapReduce
 Running an SQL Group By with
 A MapReduce Join - The Map Side
 A MapReduce Join - The Reduce Side
 A MapReduce Join - Sorting and Partitioning
 A MapReduce Join - Putting it all together

 

CH 13: K-Means Clustering
 What is K-Means Clustering?
 A MapReduce job for K-Means Clustering
 K-Means Clustering - Measuring the distance between points

 K-Means Clustering - Custom Writable for Input/output
 K-Means Clustering - Configuring the Job
 K-Means Clustering - The Mapper and Reducer

CH 14: HIVE
 Introduction
 Installing & CLI
 Hive use cases
 Architecture & Components
 Data Models
 Hive Data management
 Hive Optimization

 

CH 15: PIG
 Introduction
 Installing and Running Pig
 Pig Use Cases
 Data Model in PIG
 Multi-Data set Operations with PIG

 

CH 16: Oozie
 Introduction
 Workflow
 Examples
 Job Processing

 

CH 17: Flume
 Core Concepts
 Events
 Clients
 Agents
 Source
 Channels
 Sinks

 

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