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Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark Quick Start Guide

By : Quddus
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Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark Quick Start Guide

By: Quddus

Overview of this book

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.
Table of Contents (10 chapters)
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Summary

In this chapter, we developed Apache Kafka producer and consumer applications and utilized Spark's Structured Streaming engine to process streaming data consumed from a Kafka topic. In our real-world case study, we designed, developed, and deployed an end-to-end stream processing pipeline that was capable of consuming real tweets being authored across the world and then classified their underlying sentiment using machine learning, all of which was done in real time.

In this book, we went on both a theoretical and a hands-on journey through some of the most important and exciting technologies and frameworks that underpin the data-intelligence-driven revolution being seen across industry today. We started out by describing a new breed of distributed and scalable technologies that allow us to store, process, and analyze huge volumes of structured, semi-structured, and...

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