Taught by a 4 person team including 2 Stanford-educated, ex-Googlers and 2 ex-Flipkart Lead Analysts. This team has decades of practical experience in working with Java and with billions of rows of data. This course is a zoom-in, zoom-out, hands-on workout involving Hadoop, MapReduce and the art of thinking parallel. Let’s parse that.Zoom-in, Zoom-Out: This course is both broad and deep. It covers the individual components of Hadoop in great detail, and also gives you a higher level picture of how they interact with each other. Hands-on workout involving Hadoop, MapReduce : This course will get you hands-on with Hadoop very early on. You'll learn how to set up your own cluster using both VMs and the Cloud. All the major features of MapReduce are covered - including advanced topics like Total Sort and Secondary Sort. The art of thinking parallel: MapReduce completely changed the way people thought about processing Big Data. Breaking down any problem into parallelizable units is an art. The examples in this course will train you to "think parallel". What's Covered: Lot's of cool stuff ..Using MapReduce to: Recommend friends in a Social Networking site: Generate Top 10 friend recommendations using a Collaborative filtering algorithm. Build an Inverted Index for Search Engines: Use MapReduce to parallelize the humongous task of building an inverted index for a search engine. Generate Bigrams from text: Generate bigrams and compute their frequency distribution in a corpus of text. Build your Hadoop cluster: Install Hadoop in Standalone, Pseudo-Distributed and Fully Distributed modes Set up a hadoop cluster using Linux VMs.Set up a cloud Hadoop cluster on AWS with Cloudera Manager.Understand HDFS, MapReduce and YARN and their interaction Customize your MapReduce Jobs: Chain multiple MR jobs togetherWrite your own Customized PartitionerTotal Sort : Globally sort a large amount of data by sampling input files
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About this course
Develop advanced MapReduce applications to process BigData
Master the art of "thinking parallel" - how to break up a task into Map/Reduce transformations
Self-sufficiently set up their own mini-Hadoop cluster whether it's a single node, a physical cluster or in the cloud.
Use Hadoop + MapReduce to solve a wide variety of problems : from NLP to Inverted Indices to Recommendations
Understand HDFS, MapReduce and YARN and how they interact with each other
Understand the basics of performance tuning and managing your own cluster
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This centre's achievements
2021
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The average rating is higher than 3.7
More than 50 reviews in the last 12 months
This centre has featured on Emagister for 6 years
Subjects
Install
Team Training
Art
Linux
Windows
Computing
Course programme
Introduction
1 lecture01:52You, this course and Us We start off with an introduction on what this course is all about.
Introduction
1 lecture01:52You, this course and Us We start off with an introduction on what this course is all about.
You, this course and Us We start off with an introduction on what this course is all about.
You, this course and Us We start off with an introduction on what this course is all about.
You, this course and Us We start off with an introduction on what this course is all about.
You, this course and Us We start off with an introduction on what this course is all about.
We start off with an introduction on what this course is all about.
We start off with an introduction on what this course is all about.
Why is Big Data a Big Deal
6 lectures57:04The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB. MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
Why is Big Data a Big Deal
6 lectures57:04The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB. MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
The Big Data Paradigm Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
Big data may be a cliched term, but what does it really mean? Where does this data come from and why is it big?
Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.Serial vs Distributed Computing Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.Distributed computing makes processing very fast - but why? Let's take a simple example and see why distributed computing is so powerful.What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.
What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.
What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.
What is Hadoop? What exactly is Hadoop? Its origins and its logical components explained.
What exactly is Hadoop? Its origins and its logical components explained.
What exactly is Hadoop? Its origins and its logical components explained.
HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
HDFS or the Hadoop Distributed File System HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
HDFS based on GFS (The Google File System) is the storage layer within Hadoop. It stores files in blocks of 128MB.
MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
MapReduce Introduced MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
MapReduce is the framework which allows developers to write massively parallel programs without worrying about the underlying details of distributed computing. The developer simply implements the map() and reduce() functions in order to crunch large input sets of data.
YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
YARN or Yet Another Resource Negotiator Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
Yarn is responsible for managing resources in the Hadoop cluster. Yarn was introduced recently in Hadoop 2.0.
Installing Hadoop in a Local Environment
3 lectures36:03Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use eachHadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video. Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Installing Hadoop in a Local Environment
3 lectures36:03Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use eachHadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video. Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop Install Modes Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop has 3 different install modes - Standalone, Pseudo-distributed and Fully Distributed. Get an overview of when to use each
Hadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
Hadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
Hadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
Hadoop Standalone mode Install How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
How to set up Hadoop in the standalone mode. Windows users need to install a Virtual Linux instance before this video.
Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Hadoop Pseudo-Distributed mode Install Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
Set up Hadoop in the Pseudo-Distributed mode. All Hadoop services will be up and running!
The MapReduce "Hello World".
7 lectures01:08:47The basic philosophy underlying MapReduce In the world of MapReduce every problem can be thought of in terms of key values pairs. Map transforms the key-value pair in a meaningful way, they are sorted and merged and reduce combines key-value pairs in a meaningful way.MapReduce - Visualized And Explained If you're learning MapReduce for the very first time - it's best to visualize what exactly it does before you get down into the little details pParallelizing reduce using Shuffle And Sort...
Additional information
You'll need an IDE where you can write Java code or open the source code that's shared. IntelliJ and Eclipse are both great options.
You'll need some background in Object-Oriented Programming, preferably in Java. All the source code is in Java and we dive right in without going into Objects, Classes etc
A bit of exposure to Linux/Unix shells would be helpful, but it won't be a blocker
