Streaming Systems

With this practical guide, data engineers, data scientists, and developers will learn how to work with streaming data in a conceptual and platform-agnostic way.

Format: Paperback / softback
Length: 200 pages
Publication date: 31 August 2018
Publisher: O'Reilly Media, Inc, USA

Streaming data has become a significant aspect of big data in recent times, as businesses strive to manage the vast and ever-growing datasets that dominate our environment. In response, streaming systems have reached a level of maturity that allows for widespread adoption. This comprehensive guide is designed to assist data engineers, data scientists, and developers in effectively working with streaming data, regardless of the specific platform they are using.

The book expands upon Tyler Akidaus popular blog posts Streaming 101 and Streaming 102, providing a comprehensive and in-depth exploration of real-time data streams. It begins with an introductory level, gradually progressing to a nuanced understanding of the key concepts, principles, and techniques involved in processing streaming data.

One of the key topics covered in the book is the comparison between streaming and batch data processing patterns. The authors explain how these patterns differ in terms of their characteristics, advantages, and challenges. They also delve into the core principles and concepts behind robust out-of-order data processing, which is essential for handling the unpredictable nature of streaming data.

Watermarks are another crucial aspect of streaming data processing. The authors discuss how watermarks track progress and completeness in infinite datasets, ensuring that data is processed accurately and in a timely manner. They also explore the use of exactly-once processing techniques, which guarantee the correctness of data processing even in the presence of failures or disruptions.

The book further explains how the concepts of streams and tables form the foundations of both batch and streaming data processing. It introduces the reader to the practical motivations behind a powerful persistent state mechanism, driven by a real-world example. This mechanism allows for efficient and scalable processing of streaming data, enabling businesses to handle large volumes of data with ease.

Time-varying relations are also explored in the book, providing a link between stream processing and the world of SQL and relational algebra. The authors demonstrate how stream processing can be used to analyze and manipulate data in real-time, leveraging the power of SQL and relational algebra to perform complex operations on streaming data.

Overall, this book is a valuable resource for anyone interested in working with streaming data. It offers a comprehensive and platform-agnostic approach to understanding the what, where, when, and how of processing real-time data streams. With its detailed explanations, practical examples, and case studies, it provides a solid foundation for data engineers, data scientists, and developers to build their skills in this rapidly evolving field.

Weight: 618g
Dimension: 230 x 177 x 20 (mm)
ISBN-13: 9781491983874