Book Image

Learning Apache Flink

By : Tanmay Deshpande
Book Image

Learning Apache Flink

By: Tanmay Deshpande

Overview of this book

<p>With the advent of massive computer systems, organizations in different domains generate large amounts of data on a real-time basis. The latest entrant to big data processing, Apache Flink, is designed to process continuous streams of data at a lightning fast pace.</p> <p>This book will be your definitive guide to batch and stream data processing with Apache Flink. The book begins with introducing the Apache Flink ecosystem, setting it up and using the DataSet and DataStream API for processing batch and streaming datasets. Bringing the power of SQL to Flink, this book will then explore the Table API for querying and manipulating data. In the latter half of the book, readers will get to learn the remaining ecosystem of Apache Flink to achieve complex tasks such as event processing, machine learning, and graph processing. The final part of the book would consist of topics such as scaling Flink solutions, performance optimization and integrating Flink with other tools such as ElasticSearch.</p> <p>Whether you want to dive deeper into Apache Flink, or want to investigate how to get more out of this powerful technology, you’ll find everything you need inside.</p>
Table of Contents (17 chapters)
Learning Apache Flink
About the Author
About the Reviewers
Customer Feedback

What is machine learning?

Machine learning is a stream of engineering which uses mathematics to allow machines to make classifications, predictions, recommendations, and so on, based on the data provided to them. This area is vast, and we could spend years discussing it. But in order to keep our discussion focused, we will discuss only what is required for the scope of this book.

Very broadly, machine learning can be divided into three big categories:

  • Supervised learning

  • Unsupervised learning

  • Semi supervised learning

The preceding diagram shows a broad classification of machine learning algorithms. Now let's discuss these in detail.

Supervised learning

In supervised learning, we are generally given an input dataset, which is a historical record of actual events. We are also given what the expected output should look like. Using the historical data, we choose which factors contributed to the results. Such attributes are called features. Using the historical data, we understand how the previous results...