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

Streaming ETL and beyond


This first application will be an example of processing live streaming data with windowing and real-time visualization. The data source will be Twitter, processing of the tweet stream will compute the top hashtags in a time window as well as some counts that can be visualized as time series. The pattern is applicable to many similar use cases: data is continuously consumed from a streaming source and aggregated. Traditionally, results of such computation will land in a storage system (files, databases, and so on). Such processing can be broadly categorized as extract-transform-load (ETL) in streaming fashion. However, the focus here will be on stream processing that goes beyond the realm of general purpose ETL tools and can support streaming analytics use cases.

Stream processing needs a source of data, so every pipeline will involve the E of ETL with connector(s) to extract or ingest data (with Kafka being a common streaming source and files for batch use cases)...