

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:
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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
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Analytical and problem solving skills, applied to a Big Data environment.
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Deep understanding and related experience with Hadoop stack, HBase, Hive, Pig, Sqoop
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Hands-on experience with related/complementary open source software platforms and programming (e.g. Java, Linux)
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Good experience in writing map-reduce based algorithms and programs
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Knowledge and hands-on experience with ETL (Extract-Transform-Load) tools (e.gSqoop, Flume)
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Understanding of BI tools and reporting software and their capabilities (e.g. Business Objects)
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Sound knowledge of No-SQL databases and Relational Databases (RDBMS) as well as SQL
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Experience with agile/scrum methodologies to iterate quickly on product changes, developing user stories and working through backlogs
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Should be very analytical with ability to understand and interpret the business data
TIMINGS:
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We are providing classes during weekdays/ weekends (2-3 hrs)
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Training given by Real time expert.
Learn By Examples – Pro Hadoop
Chapter 1: Why is Big Data a Big Deal?
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Introduction
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The Big Data Paradigm
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Serial vs Distributed Computing
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What is Hadoop?
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HDFS or the Hadoop Distributed File System
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MapReduce Introduced
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YARN or Yet another Resource Negotiator
Chapter 2: Installing Hadoop in a Local Environment
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Hadoop Install Modes
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Setup a Virtual Linux Instance (For Windows users)
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Hadoop Standalone mode Install
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Hadoop Pseudo-Distributed mode Install
Chapter 3: The MapReduce "Hello World"
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The basic philosophy underlying MapReduce
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Reduce - Visualized and Explained Map
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MapReduce - Digging a little deeper at every step
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"Hello World" in MapReduce
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The Mapper
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The Reducer
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The Job
Chapter 4: Run a MapReduce Job
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Get comfortable with HDFS
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Run your first MapReduce Job
CH 5: More MapReduce - Combiners, Shuffle and Sort and Streaming API
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Parallelize the reduce phase - use the
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Not all Reducers are
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How many mappers and reducers does your MapReduce have?
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Parallelizing reduce using Shuffle and Sort
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MapReduce is not limited to the Java language - Introducing the Streaming API
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Python for MapReduce
CH 6: HDFS and Yarn
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HDFS - Protecting against data loss using replication
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HDFS - Name nodes and why they're critical
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HDFS – Check pointing to backup name node information
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Yarn - Basic components
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Yarn - Submitting a job to Yarn
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Yarn - Plug in scheduling policies
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Yarn - Configure the scheduler
CH 7: Setting up a Hadoop Cluster
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Manually configuring a Hadoop cluster (Linux VMs)
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Getting started with Amazon Web Service’s
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Start a Hadoop Cluster with Cloud era Manager on AWS
CH 8: MapReduce Customizations for Finer Grained Control
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Setting up your MapReduce to accept command line arguments
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The Tool, Tool Runner and GenericOptionsParser
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Configuring properties of the Job
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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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