Hadoop Made Very Easy
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This course teaches you Hadoop, Pig, Hive and Apache Mahout from scratch with an example based and hands on approach.
"From Scratch to Practical"
"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
"Easy to understand, makes Hadoop & Mahout simple"
"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
"Brilliant course for Data Engineers"
"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
Master the Fundamental Concepts of Big Data, Hadoop and Mahout with ease
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!
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|Section 1: Course Introduction|
Introduction to the CoursePreview
Course Outline and AgendaPreview
|Section 2: Introduction to Big Data|
Big Data - What does it mean?
Who uses Big Data, the 4Vs and trends
|Section 3: Introduction to Hadoop|
Need & Introduction to Hadoop
History & Overview of the ArchitecturePreview
|Section 4: HDFS & MapReduce overview|
Introduction to HDFS
Introduction to the MapReduce Model
Introduction to the MapReduce Architecture
|Section 5: Hadoop Installation|
Requirements, Virtual Machine install
Install Java, SSH & download HadoopPreview
Hadoop setup modes, configuring conf filesPreview
Hadoop configure and start HDFS, troubleshooting tips
|Section 6: HDFS & Map Reduce Architecture|
HDFS Playing Around - Hands on
Map Reduce Word Count program
Hadoop Reading/Writing Files & Job execution
|Section 7: Map Reduce Programming|
MapReduce Word Count Code Overview - Hands On
MapReduce Code in detail - Hands On
MapReduce Word Count Program Create Jar and Run - Hands On
MapReduce Code and Concepts - Revision
Combiner and Partitioner
|Section 8: Hadoop Streaming|
Hadoop Streaming with Example- Hands OnPreview
|Section 9: Hive|
Hive and Pig - Need & Introduction
Hive Download & Install
Hive Configuring, Partitions and Buckets
Hive Playing Around With Patent Data - Hands On
Hive: Some More Querying - Hands On
Hive Managed and External Tables
|Section 10: Pig|
Pig Setup Modes, Pig Latin and the Grunt Shell
Pig Example - Hands On
Pig Data Types & Diagnostic Operators - Hands On
Pig Operators & User Defined Functions(UDFs)
Pig Vs Hive - When to use WhichPreview
|Section 11: Introduction to Apache Mahout|
Introduction to Machine Learning & Apache Mahout
Introduction to Recommendation SystemsPreview
Collaborative Filtering Explained
How to create a recommendation system using Apache Mahout
Commonly used Similarity Algorithms
Mahout over Hadoop Movie Recommendation Example setup- Hands On
Run the Movie Recommendation system - Hands On
|Section 12: Thank You|
Thank You and Feel Free to Reach Out
With over 11 years of tech industry experience, I have been lucky to pick the brains of some of the best minds in tech and have been learning and teaching Big Data and related technologies for about 4 years now. Post becoming an entrepreneur at FeetApart, I have learnt the value of time and truly believe learning something you are passionate about is the best investment of time. I have a passion for teaching/sharing that you will see in my lectures and I go the extra mile when needed. I love long distance running and inspiring others to pick it up.