Book Image

Mastering Apache Storm

By : Ankit Jain
Book Image

Mastering Apache Storm

By: Ankit Jain

Overview of this book

Apache Storm is a real-time Big Data processing framework that processes large amounts of data reliably, guaranteeing that every message will be processed. Storm allows you to scale your data as it grows, making it an excellent platform to solve your big data problems. This extensive guide will help you understand right from the basics to the advanced topics of Storm. The book begins with a detailed introduction to real-time processing and where Storm fits in to solve these problems. You’ll get an understanding of deploying Storm on clusters by writing a basic Storm Hello World example. Next we’ll introduce you to Trident and you’ll get a clear understanding of how you can develop and deploy a trident topology. We cover topics such as monitoring, Storm Parallelism, scheduler and log processing, in a very easy to understand manner. You will also learn how to integrate Storm with other well-known Big Data technologies such as HBase, Redis, Kafka, and Hadoop to realize the full potential of Storm. With real-world examples and clear explanations, this book will ensure you will have a thorough mastery of Apache Storm. You will be able to use this knowledge to develop efficient, distributed real-time applications to cater to your business needs.
Table of Contents (19 chapters)
Title Page
About the Author
About the Reviewers
Customer Feedback

Exploring machine learning

Machine learning is a branch of applied computer science in which we build models of real-world phenomenon based on existing data available for analysis, and then using that model, predicting certain characteristics of data never seen before by the model. Machine learning has become a very important component of real-time applications as decisions need to be made in real time.

Graphically, the process of machine learning can be represented by the following figure:

The process of building the model from data is called training in machine learning terminology. Training can happen in real time on a stream of data or it can be done on historical data. When the training is done in real time, the model evolves over time with the changed data. This kind of learning is referred to as online learning, and when the model is updated every once in a while, by running the training algorithm on a new set of data, it is called offline learning.

When we talk about machine learning...