¡Oferta!

Taming Big Data with MapReduce and Hadoop – Hands On!

80,00  10,00 

Taming Big Data with MapReduce and Hadoop – Hands On!. Learn MapReduce fast by building over 10 real examples, using Python, MRJob, and Amazon’s Elastic MapReduce Service.

TAKE THIS COURSE

Descripción

The name of this course is Taming Big Data with MapReduce and Hadoop – Hands On!. The knowledge you will get with this indescribable online course is astonishing. Learn MapReduce fast by building over 10 vivo examples, using Python, MRJob, and Amazon’s Elastic MapReduce Service..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Frank Kane, one of the very best experts in this field.

Description of this course: Taming Big Data with MapReduce and Hadoop – Hands On!

Course Description “Big data” analysis is a hot and highly valuable skill – and this course will teach you two technologies fundamental to big data quickly: MapReduce and Hadoop. Ever wonder how Google manages to analyze the entire Internet on a continual basis? You’ll learn those same techniques, using your own Windows system right at home. Learn and master the art of framing data analysis problems as MapReduce problems through over 10 hands-on examples, and then scale them up to run on cloud computing services in this course. You’ll be learning from an ex-engineer and senior manager from Amazon and IMDb. Learn the concepts of MapReduce Run MapReduce jobs quickly using Python and MRJob Translate complex analysis problems into multi-stage MapReduce jobs Scale up to larger data sets using Amazon’s Elastic MapReduce service Understand how Hadoop distributes MapReduce across computing clusters Learn about other Hadoop technologies, like Hive, Pig, and Spark By the end of this course, you’ll be running code that analyzes gigabytes worth of information – in the cloud – in a matter of minutes. We’ll have some fun along the way. You’ll get warmed up with some simple examples of using MapReduce to analyze movie ratings data and text in a book. Merienda you’ve got the basics under your belt, we’ll move to some more complex and interesting tasks. We’ll use a million movie ratings to find movies that are similar to each other, and you might even discover some new movies you might like in the process! We’ll analyze a social graph of superheroes, and learn who the most “popular” superhero is – and develop a system to find “degrees of separation” between superheroes. Are all Marvel superheroes within a few degrees of being connected to The Incredible Hulk? You’ll find the answer. This course is very hands-on; you’ll spend most of your time following along with the instructor as we write, analyze, and run vivo code together – both on your own system, and in the cloud using Amazon’s Elastic MapReduce service. Over 5 hours of video content is included, with over 10 vivo examples of increasing complexity you can build, run and study yourself. Move through them at your own pace, on your own schedule. The course wraps up with an overview of other Hadoop-based technologies, including Hive, Pig, and the very hot Spark framework – complete with a working example in Spark. Don’t take my word for it – check out some of our unsolicited reviews from vivo students: “I have gone through many courses on map reduce; this is undoubtedly the best, way at the top.” “This is one of the best courses I have ever seen since 4 years passed I am using Udemy for courses.” “The best hands on course on MapReduce and Python. I really like the run it yourself approach in this course. Everything is well organized, and the lecturer is top notch.”

Requirements of this course: Taming Big Data with MapReduce and Hadoop – Hands On!

What are the requirements? You’ll need a Windows system, and we’ll walk you through downloading and installing a Python development environment and the tools you need as part of the course. If you’re on Linux and already have a Python development environment in place that you’re allegado with, that’s OK too. Again, be sure you have at least some programming or scripting experience under your belt. You won’t need to be a Python expert to succeed in this course, but you’ll need the fundamental concepts of programming in order to pick up what we’re doing.

What will you learn in this course: Taming Big Data with MapReduce and Hadoop – Hands On!?

What am I going to get from this course? Understand how MapReduce can be used to analyze big data sets Write your own MapReduce jobs using Python and MRJob Run MapReduce jobs on Hadoop clusters using Amazon Elastic MapReduce Chain MapReduce jobs together to analyze more complex problems Analyze social network data using MapReduce Analyze movie ratings data using MapReduce and produce movie recommendations with it. Understand other Hadoop-based technologies, including Hive, Pig, and Spark Understand what Hadoop is for, and how it works

Target audience of this course: Taming Big Data with MapReduce and Hadoop – Hands On!

Who is the target audience? This course is best for students with some prior programming or scripting ability. We will treat you as a beginner when it comes to MapReduce and getting everything set up for writing MapReduce jobs with Python, MRJob, and Amazon’s Elastic MapReduce service – but we won’t spend a lot of time teaching you how to write code. The focus is on framing data analysis problems as MapReduce problems and running them either locally or on a Hadoop cluster. If you don’t know Python, you’ll need to be able to pick it up based on the examples we give. If you’re new to programming, you’ll want to learn a programming or scripting language before taking this course.

Información adicional

Instructor

Frank Kane

Lectures

52

Length

5

Skill Level

All Levels

Languages

English

Includes

Lifetime access <br/> 30 day money back guarantee! <br/> Available on iOS and Android <br/> Certificate of Completion

Valoraciones

No hay valoraciones aún.

Sé el primero en valorar “Taming Big Data with MapReduce and Hadoop – Hands On!”

Tu dirección de correo electrónico no será publicada. Los campos obligatorios están marcados con *

*