Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Mastering Hadoop 3

You're reading from   Mastering Hadoop 3 Big data processing at scale to unlock unique business insights

Arrow left icon
Product type Paperback
Published in Feb 2019
Publisher Packt
ISBN-13 9781788620444
Length 544 pages
Edition 1st Edition
Languages
Tools
Concepts
Arrow right icon
Authors (3):
Arrow left icon
Timothy Wong Timothy Wong
Author Profile Icon Timothy Wong
Timothy Wong
Manish Kumar Manish Kumar
Author Profile Icon Manish Kumar
Manish Kumar
Chanchal Singh Chanchal Singh
Author Profile Icon Chanchal Singh
Chanchal Singh
Arrow right icon
View More author details
Toc

Table of Contents (21) Chapters Close

Preface 1. Section 1: Introduction to Hadoop 3 FREE CHAPTER
2. Journey to Hadoop 3 3. Deep Dive into the Hadoop Distributed File System 4. YARN Resource Management in Hadoop 5. Internals of MapReduce 6. Section 2: Hadoop Ecosystem
7. SQL on Hadoop 8. Real-Time Processing Engines 9. Widely Used Hadoop Ecosystem Components 10. Section 3: Hadoop in the Real World
11. Designing Applications in Hadoop 12. Real-Time Stream Processing in Hadoop 13. Machine Learning in Hadoop 14. Hadoop in the Cloud 15. Hadoop Cluster Profiling 16. Section 4: Securing Hadoop
17. Who Can Do What in Hadoop 18. Network and Data Security 19. Monitoring Hadoop 20. Other Books You May Enjoy

Hadoop and R

R is a data science programming tool for analyzing statistical data on models and translating analytical results into colorful graphics. R without the doubt is the most preferred programming tool for statisticians, data scientists, data analysts, and data architects, but when working with large datasets, it is short. One major disadvantage of the R programming language is that all objects are loaded into a single machine's main memory. Large petabyte size datasets cannot be loaded into the RAM. Hadoop is an ideal solution when it is integrated with R language. Data scientists must limit their data analysis to a sample of data from the large dataset to adapt to the single machine limitation of the R programming language in memory. When dealing with big data, this limitation of the R programming language is a major obstacle. Since R is not very scalable, only limited...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image