Book Image

Learning Apache Apex

By : Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles
Book Image

Learning Apache Apex

By: Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles

Overview of this book

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Summary


The project that we developed in this chapter is an example for streaming analytics. The incoming tweet stream is processed to compute aggregates which are visualized in real time with a Grafana dashboard. It shows how continuously generated data (in this case, tweets) can be analyzed and used to generate immediate insights. We have seen how existing building blocks from the Apex library (connectors, windowing) are used to accelerate application development and how integration with other infrastructure for data visualization can be accomplished.

The pipeline pattern is broadly applicable. Similar to the introductory ad-tech use case, it can be applied to other domains with data streams such as mobile, sensor, or financial transaction data. Instead of simple functionality (top words and counters), real-world applications may perform sentiment analysis, fraud detection, device health monitoring, and other complex processing.

The next chapter will go into more depth with windowing and...