Jump to content
Welcome Guest!

Join us now to get access to all our features. Once registered and logged in, you will be able to create topics, post replies to existing threads, give reputation to your fellow members, get your own private messenger, and so, so much more. It's also quick and totally free, so what are you waiting for?

This site uses cookies! Learn More

This site uses cookies!

For providing our services, we do use cookies.
But get used, this is what most of modern web do!
However we have to warn you since we are obligated to so due to EU laws.

By continuing to use this site, you agree to allow us to store cookies on your computer. :)
And no, we will not eat your computer nor you will be able to eat those cookies :P

Sign in to follow this  

Understanding the MapReduce Programming Model (2016)

Recommended Posts

Understanding the MapReduce Programming Model (2016)
September 2016 | MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 1 Hours 48M | 225 MB
Genre: eLearning | Language: English

The MapReduce programming model is the de facto standard for parallel processing of Big Data. This course introduces MapReduce, explains how data flows through a MapReduce program, and guides you through writing your first MapReduce program in Java.

Processing millions of records requires that you first understand the art of breaking down your tasks into parallel processes. The MapReduce programming model, part of the Hadoop eco-system, gives you a framework to define your solution in terms of parallel tasks, which are then combined to give you the final desired result. In this course, Understanding the MapReduce Programming Model, you'll get an introduction to the MapReduce paradigm. First, you'll learn how it helps you visualize how data flows through the map, partition, shuffle, and sort phases before it gets to the reduce phase and gives you the final result. Next, it will guide you through your very first MapReduce program in Java. Finally, you'll learn to extend the framework Mapper and Reducer classes to plug in your own logic and then run this code on your local machine without using a Hadoop cluster. By the end of this course, you will be able to break big data problems into parallel tasks to help tackle large-scale data munging operations.

Buy Premium From My Links To Get Resumable Support,Max Speed & Support Me
Download ( NitroFlare )

Download ( Uploaded

Download ( Rapidgator )

[b]Download (BigFile)[/b]

Share this post

Link to post
Share on other sites

Create an account or sign in to comment

You need to be a member in order to leave a comment

Create an account

Sign up for a new account in our community. It's easy!

Register a new account

Sign in

Already have an account? Sign in here.

Sign In Now

Sign in to follow this