Mode of learning : Online - Instructor Lead(LVC)
Domain / Subject : Engineering & Technology
Function : Information Technology(IT)
Starts on : 2nd Aug 2014
Duration : 5 Weeks
Difficulty : Basic
Big Data and Hadoop training course is designed to provide knowledge and skills to become a successful Hadoop Developer. In-depth knowledge of concepts such as Hadoop Distributed File System, Hadoop Cluster, Map-Reduce, Hbase Zookeeper etc. will be covered in the course.
After the completion of the Big Data and Hadoop Course at Edureka, you should be able to:
Master the concepts of Hadoop Distributed File System and MapReduce framework
Setup a Hadoop Cluster
Understand Data Loading Techniques using Sqoop and Flume
Program in MapReduce (Both MRv1 and MRv2)
Learn to write Complex MapReduce programs
Program in YARN (MRv2)
Perform Data Analytics using Pig and Hive
Implement HBase, MapReduce Integration, Advanced Usage and Advanced Indexing
Have a good understanding of ZooKeeper service
New features in Hadoop 2.0 -- YARN, HDFS Federation, NameNode High Availability
Implement best Practices for Hadoop Development and Debugging
Implement a Hadoop Project
Work on a Real Life Project on Big Data Analytics and gain Hands on Project Experience
Who should go for this course?
This course is designed for professionals aspiring to make a career in Big Data Analytics using Hadoop Framework. Software Professionals, Analytics Professionals, ETL developers, Project Managers, Testing Professionals are the key beneficiaries of this course. Other professionals who are looking forward to acquire a solid foundation of Hadoop Architecture can also opt for this course.
Towards the end of the Course, you will be working on a live project which will be a large dataset and you will be using PIG, HIVE, HBase and MapReduce to perform Big Data analytics. The final project is a real life business case on some open data set. There is not one but a large number of datasets which are a part of the Big Data and Hadoop Program.
Here are some of the data sets on which you may work as a part of the project work:
Twitter Data Analysis : Twitter data analysis is used to understand the hottest trends by dwelling into the twitter data. Using flume data is fetched from twitter to Hadoop in JSON format. Using JSON-serde twitter data is read and fed into HIVE tables so that we can do different analysis using HIVE queries. For eg: Top 10 popular tweets etc.
Stack Exchange Ranking and Percentile data-set : Stack Exchange is a place where you will find enormous data from multiple websites of Stack Group (like: stack overflow) which is open sourced. The place is a gold mine for people who wants to come up with several POC’s and are searching for suitable data-sets. In there you may query out the data you are interested in which will contain more than 50,000 odd records. For eg: You can download StackOverflow Rank and Percentile data and find out the top 10 rankers.
Loan Dataset : The project is designed to find the good and bad URL links based on the reviews given by the users. The primary data will be highly unstructured. Using MR jobs the data will be transformed into structured form and then pumped to HIVE tables. Using Hive queries we can query out the information very easily. In the phase two we will feed another dataset which contains the corresponding cached web pages of the URL’s into HBASE. Finally the entire project is showcased into a UI where you can check the ranking of the URL and view the cached page.
Data -sets by Government: These Data sets could be like Worker Population Ratio (per 1000) for persons of age (15-59) years according to the current weekly status approach for each state/UT.
Machine Learning Dataset like Badges datasets : Such dataset is for system to encode names, for example +/- label followed by a person’s name.
NYC Data Set: NYC Data Set contains the day to day records of all the stocks. It will provide you with the information like opening rate, closing rate, etc for individual stocks. Hence, this data is highly valuable for people you have to make decision based on the market trends. One of the analysis which is very popular and can be done on this data set is to find out the Simple Moving Average which helps them to find the crossover action.
Weather Dataset : It has all the details of weather over a period of time using which you may find out the highest, lowest or average temperature.
In addition, you can choose your own dataset and create a project around that as well.
BiG Data! A Worldwide Problem?
According to Wikipedia, “Big data is a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.” In simpler terms, Big Data is a term given to large volumes of data that organizations store and process. However, It is becoming very difficult for companies to store, retrieve and process the ever-increasing data. If any company gets hold on managing its data well, nothing can stop it from becoming the next BIG success!
The problem lies in the use of traditional systems to store enormous data. Though these systems were a success a few years ago, with increasing amount and complexity of data, these are soon becoming obsolete. The good news is - Hadoop, which is not less than a panacea for all those companies working with BIG DATA in a variety of applications has become an integral part for storing, handling, evaluating and retrieving hundreds or even petabytes of data.
Apache Hadoop! A Solution for Big Data!
Hadoop is an open source software framework that supports data-intensive distributed applications. Hadoop is licensed under the Apache v2 license. It is therefore generally known as Apache Hadoop. Hadoop has been developed, based on a paper originally written by Google on MapReduce system and applies concepts of functional programming. Hadoop is written in the Java programming language and is the highest-level Apache project being constructed and used by a global community of contributors. Hadoop was developed by Doug Cutting and Michael J. Cafarella. And just don’t overlook the charming yellow elephant you see, which is basically named after Doug’s son’s toy elephant!
Some of the top companies using Hadoop:
The importance of Hadoop is evident from the fact that there are many global MNCs that are using Hadoop and consider it as an integral part of their functioning, such as companies like Yahoo and Facebook! On February 19, 2008, Yahoo! Inc. established the world's largest Hadoop production application. The Yahoo! Search Webmap is a Hadoop application that runs on over 10,000 core Linux cluster and generates data that is now widely used in every Yahoo! Web search query.
Facebook, a $5.1 billion company has over 1 billion active users in 2012, according to Wikipedia. Storing and managing data of such magnitude could have been a problem, even for a company like Facebook. But thanks to Apache Hadoop! Facebook uses Hadoop to keep track of each and every profile it has on it, as well as all the data related to them like their images, posts, comments, videos, etc.
Opportunities for Hadoopers!
Opportunities for Hadoopers are infinite - from a Hadoop Developer, to a Hadoop Tester or a Hadoop Architect, and so on. If cracking and managing BIG Data is your passion in life, then think no more and Join Edureka’s Hadoop Online course and carve a niche for yourself! Happy Hadooping!
|Start Date||Duration||Class Days||Class Time (IST)||Price (INR)|
07:00AM - 10:00AM IST
08:30 PM - 11:30 PM IST
Mon, Tue,Wed, Thu, Fri
07:00AM - 09:00 AM IST
07:00AM - 10:00 AM IST
|01 September||15 Days||Mon, Tue,Wed, Thu, Fri||07:00AM-09:00 AM IST||16,996|
Kindly Note:Early Bird Offer for Date 30 August & 01 September
Some of the prerequisites for learning Hadoop include hands-on experience in Core Java and good analytical skills to grasp and apply the concepts in Hadoop. We provide a complimentary Course "Java Essentials for Hadoop" to all the participants who enroll for the Hadoop Training. This course helps you brush up your Java Skills needed to write Map Reduce programs.
Learning Objectives - In this module, you will understand what is Big Data, What are the limitations of the existing solutions for Big Data problem, How Hadoop solves the Big Data problem, What are the common Hadoop ecosystem components, Hadoop Architecture, HDFS and Map Reduce Framework, and Anatomy of File Write and Read.
Topics - What is Big Data, Hadoop Architecture, Hadoop ecosystem components, Hadoop Storage: HDFS, Hadoop Processing: MapReduce Framework, Hadoop Server Roles: NameNode, Secondary NameNode, and DataNode, Anatomy of File Write and Read.
Hadoop Cluster Configuration and Data Loading
Learning Objectives - In this module, you will learn the Hadoop Cluster Architecture and Setup, Important Configuration files in a Hadoop Cluster, Data Loading Techniques.
Topics - Hadoop Cluster Architecture, Hadoop Cluster Configuration files, Hadoop Cluster Modes, Multi-Node Hadoop Cluster, A Typical Production Hadoop Cluster, MapReduce Job execution, Common Hadoop Shell commands, Data Loading Techniques: FLUME, SQOOP, Hadoop Copy Commands, Hadoop Project: Data Loading.
Hadoop MapReduce framework
Learning Objectives - In this module, you will understand Hadoop MapReduce framework and how MapReduce works on data stored in HDFS. Also, you will learn what are the different types of Input and Output formats in MapReduce framework and their usage.
Topics - Hadoop Data Types, Hadoop MapReduce paradigm, Map and Reduce tasks, MapReduce Execution Framework, Partitioners and Combiners, Input Formats (Input Splits and Records, Text Input, Binary Input, Multiple Inputs), Output Formats (TextOutput, BinaryOutPut, Multiple Output), Hadoop Project: MapReduce Programming.
Learning Objectives - In this module, you will learn Advance MapReduce concepts such as Counters, Schedulers, Custom Writables, Compression, Serialization, Tuning, Error Handling, and how to deal with complex MapReduce programs.
Topics - Counters, Custom Writables, Unit Testing: JUnit and MRUnit testing framework, Error Handling, Tuning, Advance MapReduce, Hadoop Project: Advance MapReduce programming and error handling.
Pig and Pig Latin
Learning Objectives - In this module, you will learn what is Pig, in which type of use case we can use Pig, how Pig is tightly coupled with MapReduce, and Pig Latin scripting.
Topics - Installing and Running Pig, Grunt, Pig's Data Model, Pig Latin, Developing & Testing Pig Latin Scripts, Writing Evaluation, Filter, Load & Store Functions, Hadoop Project: Pig Scripting.
Hive and HiveQL
Learning Objectives - This module will help you in understanding Apache Hive Installation, Loading and Querying Data in Hive and so on.
Topics - Hive Architecture and Installation, Comparison with Traditional Database, HiveQL: Data Types, Operators and Functions, Hive Tables(Managed Tables and External Tables, Partitions and Buckets, Storage Formats, Importing Data, Altering Tables, Dropping Tables), Querying Data (Sorting And Aggregating, Map Reduce Scripts, Joins & Subqueries, Views, Map and Reduce side Joins to optimize Query).
Advance Hive, NoSQL Databases and HBase
Learning Objectives - In this module, you will understand Advance Hive concepts such as UDF. You will also acquire in-depth knowledge of what is HBase, how you can load data into HBase and query data from HBase using client.
Topics - Hive: Data manipulation with Hive, User Defined Functions, Appending Data into existing Hive Table, Custom Map/Reduce in Hive, Hadoop Project: Hive Scripting, HBase: Introduction to HBase, Client API's and their features, Available Client, HBase Architecture, MapReduce Integration.
Advance HBase and ZooKeeper
Learning Objectives - This module will cover Advance HBase concepts. You will also learn what Zookeeper is all about, how it helps in monitoring a cluster, why HBase uses Zookeeper and how to Build Applications with Zookeeper.
Topics - HBase: Advanced Usage, Schema Design, Advance Indexing, Coprocessors, Hadoop Project: HBase tables The ZooKeeper Service: Data Model, Operations, Implementation, Consistency, Sessions, States.
Hadoop 2.0, MRv2 and YARN
Learning Objectives - In this module, you will understand the newly added features in Hadoop 2.0, namely, YARN, MRv2, NameNode High Availability, HDFS Federation, support for Windows etc.
Topics - Schedulers:Fair and Capacity, Hadoop 2.0 New Features: NameNode High Availability, HDFS Federation, MRv2, YARN, Running MRv1 in YARN, Upgrade your existing MRv1 code to MRv2, Programming in YARN framework.
Hadoop Project Environment and Apache Oozie
Learning Objectives - In this module, you will understand how multiple Hadoop ecosystem components work together in a Hadoop implementation to solve Big Data problems. We will discuss multiple data sets and specifications of the project. This module will also cover Apache Oozie Workflow Scheduler for Hadoop Jobs.
Some of the data sets on which you may work as a part of the project work:
Twitter Data Analysis : Download twitter data and the put it in HBase and use Pig, Hive and MapReduce to garner the popularity of some hashtags
Stack Exchange Ranking and Percentile data-set : It is dataset from StackOverFlow, in which there ranking and percentile details of Users
Loan Dataset : It deals with the users who has taken along with their Emi details, time period etc
Data -sets by Government: Like Worker Population Ratio (per 1000) for persons of age (15-59) years according to the current weekly status approach for each state/UT
Machine Learning Dataset like Badges datasets : The dataset is for system toencode names , for eg. +/- label followed by a persons name
NYC Data Set: New York Stock Exchange data
Weather Dataset : It has all the details of weather over a period of time using which you may find out the hottest or coldest or average temperature
In addition, you can choose your own dataset and create a project around that as well.
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