Wednesday, 26 October 2016

Hadoop Best Tutorial

Course Description

"Huge information" investigation is a hot and very important ability – and this course will show both of you advances basic to huge information rapidly: MapReduce and Hadoop. Ever consider how Google figures out how to dissect the whole Internet on a consistent premise? You'll take in those same strategies, utilizing your own Windows framework comfortable.

Learn and ace the craft of surrounding information investigation issues as MapReduce issues through more than 10 hands-on cases, and after that scale them up to keep running on distributed computing administrations in this course. You'll be gaining from an ex-architect and senior supervisor from Amazon and IMDb.

•  Learn the ideas of MapReduce

•  Run MapReduce occupations rapidly utilizing Python and MRJob

•  Translate complex investigation issues into multi-organize MapReduce employments

•  Scale up to bigger information sets utilizing Amazon's Elastic MapReduce benefit

•  Understand how Hadoop appropriates MapReduce crosswise over registering groups

•  Learn about other Hadoop innovations, similar to Hive, Pig, and Spark

Before the end of this course, you'll be running code that breaks down gigabytes worth of data – in the cloud – in a matter of minutes.

We'll have a great time en route. You'll get warmed up with some straightforward cases of utilizing MapReduce to break down motion picture appraisals information and content in a book. Once you have the fundamentals added to your repertoire, we'll move to some more intricate and intriguing undertakings. We'll utilize a million motion picture evaluations to discover motion pictures that are like each other, and you may considerably find some new films you may like all the while! We'll investigate a social chart of superheroes, and realize who the most "well known" superhero is – and build up a framework to discover "degrees of detachment" between superheroes. Are all Marvel superheroes inside a couple of degrees of being associated with The Incredible Hulk? You'll discover the reply.

This Hadoop Tutorial isexceptionally involved; you'll invest the greater part of your energy taking after alongside the educator as we compose, break down, and run genuine code together – both all alone framework, and in the cloud utilizing Amazon's Elastic MapReduce benefit. More than 5 hours of video substance is incorporated, with more than 10 genuine cases of expanding intricacy you can assemble, run and study yourself. Travel through them at your own pace, all alone calendar. The course wraps up with a review of other Hadoop-based advancements, including Hive, Pig, and the exceptionally hot Spark structure – finish with a working case in Spark.

Try not to trust me - look at some of our spontaneous surveys from genuine understudies:

"I have experienced numerous courses on guide decrease; this is without a doubt the best, route at the top."

"This is one of the best courses I have ever observed since 4 years passed I am utilizing Udemy for courses."

"The best hands on course on MapReduce and Python. I truly like the run it yourself approach in this course. Everything is all around composed, and the speaker is choice."



• You'll require a Windows framework, and we'll walk you through downloading and introducing a Python improvement environment and the devices you require as a major aspect of the course. In case you're on Linux and as of now have a Python improvement environment set up that you're acquainted with, that is OK as well. Once more, make certain you have at any rate some programming or scripting background added to your repertoire. You won't should be a Python master to prevail in this course, however you'll require the principal ideas of programming keeping in mind the end goal to get what we're doing.


• Understand how MapReduce can be utilized to break down enormous information sets

• Write your own particular MapReduce employments utilizing Python and MRJob

• Run MapReduce employments on Hadoop groups utilizing Amazon Elastic MapReduce

•  Chain MapReduce employments together to break down more mind boggling issues

•  Analyze informal organization information utilizing MapReduce

• Analyze film evaluations information utilizing MapReduce and deliver motion picture proposals with it.

•  Understand other Hadoop-based innovations, including Hive, Pig, and Spark

•  Understand what Hadoop is really going after, how it works



• This course is best for understudies with some earlier programming or scripting capacity. We will regard you as a tenderfoot with regards to MapReduce and getting everything set up for composing MapReduce occupations with Python, MRJob, and Amazon's Elastic MapReduce benefit - however we won't invest a great deal of energy showing you how to compose code. The emphasis is on confining information investigation issues as MapReduce issues and running them either locally or on a Hadoop group. On the off chance that you don't know Python, you'll should have the capacity to lift it up in light of the illustrations we give. In case you're new to programming, you'll need to take in a programming or scripting dialect before taking this course.

Educational programs

Segment 1: Introduction, and Getting Started
Lecture 1     Introduction
Lecture 2     Getting Started - Run your First MapReduce Program!

Segment 2: Understanding MapReduce
Lecture 3     MapReduce Basic Concepts
Lecture 4     A fast note on record names.   Article
Lecture 5     Walkthrough of Rating Histogram Code
Lecture 6     Understanding How MapReduce Scales/Distributed Computing
Lecture 7     Average Friends by Age Example: Part 1
Lecture 8     Average Friends by Age Example: Part 2
Lecture 9     Minimum Temperature By Location Example
Lecture 10   Maximum Temperature By Location Example
Lecture 11   Word Frequency in a Book Example
Lecture 12   Making the Word Frequency Mapper Better with Regular Expressions
Lecture 13   Sorting the Word Frequency Results Using Multi-Stage MapReduce Jobs
Lecture 14   Activity: Design a Mapper and Reducer for Total Spent by Customer
Lecture 15   Activity: Write Code for Total Spent by Customer
Lecture 16   Compare Your Code to Mine. Movement: Sort Results by Amount Spent
Lecture 17   Compare your Code to Mine for Sorted Results.
Lecture 18   Combiners

Segment 3: Advanced MapReduce Examples
Lecture 19   Example: Most Popular Movie
Lecture 20   Including Ancillary Lookup Data in the Example
Lecture 21   Example: Most Popular Superhero, Part 1
Lecture 22   Example: Most Popular Superhero, Part 2
Lecture 23   Example: Degrees of Separation: Concepts
Lecture 24   Degrees of Separation: Preprocessing the Data
Lecture 25   Degrees of Separation: Code Walkthrough
Lecture 26   Degrees of Separation: Running and Analyzing the Results
Lecture 27   Example: Similar Movies Based on Ratings: Concepts
Lecture 28   Similar Movies: Code Walkthrough
Lecture 29   Similar Movies: Running and Analyzing the Results
Lecture 30   Learning Activity: Improving our Movie Similarities MapReduce Job

Segment 4: Using Hadoop and Elastic MapReduce
Lecture 31   Fundamental Concepts of Hadoop
Lecture 32   The Hadoop Distributed File System (HDFS)
Lecture 33   Apache YARN
Lecture 34   Hadoop Streaming: How Hadoop Runs your Python Code
Lecture 35   Setting Up Your Amazon Elastic MapReduce Account
Lecture 36   Linking Your EMR Account with MRJob
Lecture 37   Exercise: Run Movie Recommendations on Elastic MapReduce
Lecture 38   Analyze the Results of Your EMR Job

Segment 5: Advanced Hadoop and EMR
Lecture 39   Distributed Computing Fundamentals
Lecture 40   Activity: Running Movie Similarities on Four Machines
Lecture 41   Analyzing the Results of the 4-Machine Job
Lecture 42   Troubleshooting Hadoop Jobs with EMR and MRJob, Part 1
Lecture 43   Troubleshooting Hadoop Jobs, Part 2
Lecture 44   Analyzing One Million Movie Ratings Across 16 Machines, Part 1
Lecture 45   Analyzing One Million Movie Ratings Across 16 Machines, Part 2

Segment 6: Other Hadoop Technologies
Lecture 46   Introducing Apache Hive
Lecture 47   Introducing Apache Pig
Lecture 48   Apache Spark: Concepts
Lecture 49   Spark Example: Part 1
Lecture 50   Spark Example: Part 2
Lecture 51   Congratulations!

Segment 7: Where to Go from Here

Lecture 52   Bonus Lecture: Discounts on my other courses!