Big data processing, microservices, and cloud-native technology are a match made in computing heaven, enabling microservices to be used to build a flexible, scalable, and distributed system of loosely coupled data processing tasks, called data services.
Mario-Leander Reimer explores key JEE technologies that can be used to build JEE-powered data services and walks you through implementing the individual data processing tasks of a simplified showcase application. You’ll then deploy and orchestrate the individual data services using OpenShift, illustrating the scalability of the overall processing pipeline. The context and content is taken from a real-world project for a major German car manufacturer, implementing a microservices-based processing pipeline that uses car-related event data (sensor data, traffic events, and other real-time data) for a traffic information management and route optimization system.
M.-Leander Reimer is a chief technologist for QAware GmbH. A senior Java developer with several years of experience designing complex and large-scale system architectures, he is continuously looking for innovative ways to combine and apply state-of-the-art technology and open source software components in real-world customer projects. As a member of the JCP, he wants to improve Java technology and develop usable technical specifications. He studied computer science at Rosenheim and Staffordshire University.
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