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Event Stream Processing

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May 11, 2024 3 minute read

Event Stream Processing (ESP) is a real-time data processing paradigm that enables organizations to gain insights from data in its raw form, before it is stored in a database or data warehouse. ESP systems are designed to handle high volumes of data that is generated continuously from various sources, such as sensors, IoT devices, social media platforms, and transaction logs. This data is typically unstructured and may contain a mix of event types, making it challenging to analyze using traditional methods.

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Reading list

We've selected seven books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Event Stream Processing.
Covers various big data analytics techniques, including event stream processing, using Java as the programming language.
Focuses on Apache Camel, an integration framework, in the context of event-driven architecture and event stream processing.
Includes a section on stream processing using Scala, a programming language well-suited for concurrent and distributed computing.
Covers real-time data processing and event stream processing using Node.js, a popular JavaScript runtime environment.
Includes a chapter on event stream processing using Python, providing practical guidance for implementing streaming data pipelines.
Discusses the concept of data mesh architecture, which includes principles and practices for managing and processing data in a decentralized and event-driven manner.
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