Hadoop Made Very Easy
Hadoop Made Very Easy, available at $19.99, has an average rating of 3.45, with 47 lectures, based on 47 reviews, and has 1138 subscribers.
You will learn about Easily crack Big Data interviews & get ready for Hadoop Certification exams! Get practical knowledge of Big Data, Hadoop and Mahout without spending too much time and money and become a high paying Big Data resource! Program in Map Reduce, Hive, Pig and Apache Mahout Pick up deeper details of Map Reduce, Machine Learning and Data Science quickly Learn about personalization with Apache Mahout and building recommendation systems This course is ideal for individuals who are Any one interested in learning about Big Data and Hadoop concepts or Any one interested in learning about Apache Mahout and how to build recommendation systems using Mahout over Hadoop or Any one wanting to clear the Hadoop Certification exam or Any one wanting to clear Big Data related interviews with ease It is particularly useful for Any one interested in learning about Big Data and Hadoop concepts or Any one interested in learning about Apache Mahout and how to build recommendation systems using Mahout over Hadoop or Any one wanting to clear the Hadoop Certification exam or Any one wanting to clear Big Data related interviews with ease.
Enroll now: Hadoop Made Very Easy
Summary
Title: Hadoop Made Very Easy
Price: $19.99
Average Rating: 3.45
Number of Lectures: 47
Number of Published Lectures: 47
Number of Curriculum Items: 47
Number of Published Curriculum Objects: 47
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- Easily crack Big Data interviews & get ready for Hadoop Certification exams!
- Get practical knowledge of Big Data, Hadoop and Mahout without spending too much time and money and become a high paying Big Data resource!
- Program in Map Reduce, Hive, Pig and Apache Mahout
- Pick up deeper details of Map Reduce, Machine Learning and Data Science quickly
- Learn about personalization with Apache Mahout and building recommendation systems
Who Should Attend
- Any one interested in learning about Big Data and Hadoop concepts
- Any one interested in learning about Apache Mahout and how to build recommendation systems using Mahout over Hadoop
- Any one wanting to clear the Hadoop Certification exam
- Any one wanting to clear Big Data related interviews with ease
Target Audiences
- Any one interested in learning about Big Data and Hadoop concepts
- Any one interested in learning about Apache Mahout and how to build recommendation systems using Mahout over Hadoop
- Any one wanting to clear the Hadoop Certification exam
- Any one wanting to clear Big Data related interviews with ease
This course teaches you Hadoop, Pig, Hive and Apache Mahout from scratch with an example based and hands on approach.
"From Scratch to Practical"
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"This course is hell awesome, if you are new to Hadoop this course is for you, from theory to hands on experience , plus a Mahout and recommended system as Project. This course is a five star.!!!" – Aakash
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"Easy to understand, makes Hadoop & Mahout simple"
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"This course has helped me crack a couple of Big Data engineer interviews as the basics are well explained here. The video/audio quality is fine and the instructor knows his stuff!"- Shipra
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"Brilliant course for Data Engineers"
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"This is course is well structured. I would like to call this Big Data and Hadoop for Dummies. It covers basics as well as advanced concepts in a very unique way. Hands on examples gave me clear direction about how to use Hadoop in production environment. I strongly recommend this course to all levels of data engineers and Big data enthusiasts.Production quality is good." – Ashrith
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Master the Fundamental Concepts of Big Data, Hadoop and Mahout with ease
- Understand the Big Data & Apache Hadoop landscape
- Learn HDFS & MapReduce concepts with examples and hands on labs
- Learn Hadoop Streaming
- Understand Analytics with Hadoop using Pig and Hive
- Machine Learning Concepts
- Collaborative Filtering with Apache Mahout
- Real world Recommender System with Mahout and Hadoop
Big Data and Data Science Foundation to empower you with the most specialized skills
The core concepts are stressed upon and the focus is on building a solid foundation of the key Hadoop, Map Reduce and collaborative filtering concepts upon which you can learn just about every other technology in the same space. Preliminary Java and Unix knowledge is expected.
Contents & Overview
Through 47 lectures and 8 hours of content, we will take a step-by step approach to understanding Big Data and related concepts from scratch.
The first few topics will focus on the rise of Big Data and how Apache Hadoop fits in. We will focus on the fundamentals of Hadoop and its core components: HDFS and Map Reduce. We will then setup and play around with Hadoop and HDFS and then deep dive into MapReduce programming with hands on examples. We will also spend time on Combiners and Partitioners and how they can help. We will also spend time on Hadoop Streaming: a tool that helps non-Java professionals to leverage the power of Hadoop and do POCs on it.
Once we have a solid foundation of HDFS and MapReduce, in the next couple of topics we will explore higher level components of the Hadoop ecosystem: Hive and Pig. We will go into the details of both Hive and Pig by installing them and working with examples. Hive and Pig can make your life easy by shielding you from the complexity of writing MR jobs and yet leveraging the parallel processing ability of the Hadoop framework.
In the next few lectures we will look at something very interesting: Apache Mahout and Machine Learning. Apache Mahout is a Java library that lets you write machine learning applications with ease. We will learn the basics of Machine Learning and go deeper into Collaborative Filtering and recommender systems, something that Mahout excels that.
We will look at some similarity algorithms, understand their real-life implications and apply them when we will build together a real world movie recommender system using Mahout and Hadoop.
After taking this course, which includes slides, examples, code and data sets, you will be at ease with playing aroundwith HDFS, writing MapReduce jobs, analyzing data with Hive and Pig, and building a recommender system using Apache Mahout. So go ahead and enroll to crack that Big Data/Data Science interview and clear that certification exam!
Course Curriculum
Chapter 1: Course Introduction
Lecture 1: Introduction to the Course
Lecture 2: Course Outline and Agenda
Chapter 2: Introduction to Big Data
Lecture 1: Big Data – What does it mean?
Lecture 2: Who uses Big Data, the 4Vs and trends
Chapter 3: Introduction to Hadoop
Lecture 1: Need & Introduction to Hadoop
Lecture 2: History & Overview of the Architecture
Chapter 4: HDFS & MapReduce overview
Lecture 1: Introduction to HDFS
Lecture 2: Introduction to the MapReduce Model
Lecture 3: Introduction to the MapReduce Architecture
Chapter 5: Hadoop Installation
Lecture 1: Requirements, Virtual Machine install
Lecture 2: Install Java, SSH & download Hadoop
Lecture 3: Hadoop setup modes, configuring conf files
Lecture 4: Hadoop configure and start HDFS, troubleshooting tips
Lecture 5: MapReduce Daemons
Chapter 6: HDFS & Map Reduce Architecture
Lecture 1: HDFS Architecture
Lecture 2: HDFS Playing Around – Hands on
Lecture 3: Map Reduce Word Count program
Lecture 4: Hadoop Reading/Writing Files & Job execution
Chapter 7: Map Reduce Programming
Lecture 1: MapReduce Word Count Code Overview – Hands On
Lecture 2: MapReduce Code in detail – Hands On
Lecture 3: MapReduce Word Count Program Create Jar and Run – Hands On
Lecture 4: MapReduce Code and Concepts – Revision
Lecture 5: Combiner and Partitioner
Chapter 8: Hadoop Streaming
Lecture 1: Hadoop Streaming with Example- Hands On
Chapter 9: Hive
Lecture 1: Hive and Pig – Need & Introduction
Lecture 2: Hive Introduction
Lecture 3: Hive Architecture
Lecture 4: Hive Download & Install
Lecture 5: Hive Configuring, Partitions and Buckets
Lecture 6: Hive Playing Around With Patent Data – Hands On
Lecture 7: Hive: Some More Querying – Hands On
Lecture 8: Hive Managed and External Tables
Chapter 10: Pig
Lecture 1: Pig Introduction
Lecture 2: Pig Setup Modes, Pig Latin and the Grunt Shell
Lecture 3: Pig Example – Hands On
Lecture 4: Pig Data Types & Diagnostic Operators – Hands On
Lecture 5: Pig Operators & User Defined Functions(UDFs)
Lecture 6: Pig Vs Hive – When to use Which
Chapter 11: Introduction to Apache Mahout
Lecture 1: Introduction to Machine Learning & Apache Mahout
Lecture 2: Introduction to Recommendation Systems
Lecture 3: Collaborative Filtering Explained
Lecture 4: How to create a recommendation system using Apache Mahout
Lecture 5: Commonly used Similarity Algorithms
Lecture 6: Mahout over Hadoop Movie Recommendation Example setup- Hands On
Lecture 7: Run the Movie Recommendation system – Hands On
Lecture 8: Mahout Conclusion
Chapter 12: Thank You
Lecture 1: Thank You and Feel Free to Reach Out
Instructors
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Abhishek Roy
Experienced Big Data trainer, technologist & entrepreneur
Rating Distribution
- 1 stars: 3 votes
- 2 stars: 5 votes
- 3 stars: 11 votes
- 4 stars: 13 votes
- 5 stars: 15 votes
Frequently Asked Questions
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