Hadoop Made Very Easy
Hadoop Made Very Easy
MP4 | Video: 1280x720 | 83 kbps | 44 KHz | Duration: 9 Hours | 1.11 GB
Genre: eLearning | Language: English
MP4 | Video: 1280x720 | 83 kbps | 44 KHz | Duration: 9 Hours | 1.11 GB
Genre: eLearning | Language: English
Learn Hadoop, Pig, Hive and Mahout with a hands on approach without spending too much time and boost your career
This course teaches you Hadoop, Pig, Hive and Apache Mahout from scratch with an example based and hands on approach.
"From Scratch to Practical"
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!
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!
Hadoop Made Very Easy
