Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning with Apache Spark Quick Start Guide

You're reading from   Machine Learning with Apache Spark Quick Start Guide Uncover patterns, derive actionable insights, and learn from big data using MLlib

Arrow left icon
Product type Paperback
Published in Dec 2018
Publisher Packt
ISBN-13 9781789346565
Length 240 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Author (1):
Arrow left icon
Jillur Quddus Jillur Quddus
Author Profile Icon Jillur Quddus
Jillur Quddus
Arrow right icon
View More author details
Toc

Distributed streaming platform

So far in this book, we have been performing batch processing—that is, we have been provided with bounded raw data files and processed that data as a group. As we saw in Chapter 1, The Big Data Ecosystem, stream processing differs from batch processing in the fact that data is processed as and when individual units, or streams, of data arrive. We also saw in Chapter 1, The Big Data Ecosystem, how Apache Kafka, as a distributed streaming platform, allows us to move real-time data between systems and applications in a fault-tolerant and reliable manner via a logical streaming architecture comprising of the following components:

  • Producers: Applications that generate and send messages
  • Consumers: Applications that subscribe to and consume messages
  • Topics: Streams of records belonging to a particular category and stored as a sequence of ordered...
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 R$50/month. Cancel anytime