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Learning Apache Apex

Learning Apache Apex

By : Gundabattula, Thomas Weise, Munagala V. Ramanath, David Yan, Kenneth Knowles
5 (1)
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Learning Apache Apex

Learning Apache Apex

5 (1)
By: Gundabattula, Thomas Weise, 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 (11 chapters)
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The application pattern in a real-world use case


One of the case studies mentioned in the introductory chapter was an ad-tech use case. The Apex-based system powering that use case went into production in 2014 and fits the same pattern of the example that we will build in this chapter. Summarized at a high level, the system consumes log records from Apache Kafka, then aggregates these records into a dimensional model and provides the aggregates to a real-time dashboard that users can use to gain actionable insights into the performance of advertising campaigns. Time to insight is critical, as the ability to perform timely adjustments often translates to incremental revenue or reduced cost (or both) for the business:

The Apex-based implementation replaced a batch system that involved several hours of latency with a streaming pipeline that reduced latency to seconds (end-to-end, including data collection and ingestion). The dimension store holds the aggregated data in memory (conceptually,...

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