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The Guide To Event Stream Processing


Real-time applications require technologies capable of real-time data analysis. These kinds of applications are used for social media, the stock market, fraud detection, fleet management, traffic reporting, and customer service use cases. While real-time technology is a core part of improving customer experiences, it can also play a major role in critical moments.

Autonomous vehicles generate a vast amount of data from cameras, radars, light detection and ranging (LIDAR) sensors (​​measure distances by pulsing lasers), and global positioning systems. Self-driving cars must analyze this data in real-time to obtain a three-dimensional view of their surroundings and avoid obstacles while navigating to a destination. Anything less than real-time increases risk.

Supply chain and logistics management applications rely on barcode scanners and RFID (radio frequency identification) to determine the physical location of raw materials and finished goods traveling between suppliers, manufacturers, and consumers. They use this data to estimate delivery time, calculate inventory, and detect loss or theft. Delays can have vast impacts across an organization's critical systems.

The analytical engines supporting such real-time applications rely on two modern concepts covered in this guide: Stream processing and complex event processing (CEP). Some refer to the combination of the two as event stream processing. 

Data stream processing diagram

Modern real-time applications can’t afford delays. Starting in the 1990s, computer scientists conceived paradigms to analyze data in parallel pipelines as it streams in real-time (a.k.a. stream processing). The complex processing of streaming events relies on several techniques, such as pattern detection, aggregation, relationship modeling, and correlation, to support real-time decisions (a.k.a. complex event processing).

Event stream processing diagram

This guide explains stream processing and complex event processing concepts and reviews the software technologies used for their practical implementation. We aim to help enterprises overcome the challenges of implementing global stateful applications that require the lowest possible data processing latencies.

Having a thorough understanding of the technology landscape is essential, whether you are developing applications or considering ready-to-go industry or customized solutions. By acquiring comprehensive knowledge about these technology options, you can make well-informed decisions and effectively harness the most suitable solution to cater to your unique streaming and complex event processing demands.

If you want to learn about how Macrometa addresses stream processing and complex event processing, please review this blog.

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