Complex Event Processing

Complex event processing (CEP) analyzes data from multiple data sources/events in real-time to look for insights and patterns.

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What is Complex Event Processing?

Complex event processing (CEP) is a powerful tool used for querying data in real-time. CEP can process and analyze data from multiple events as they happen. CEP is often used interchangeably with Event stream processing (ESP), a practice that also processes event-driven information. CEP is also described as a stream that assists in querying data elements during the storing process within a database but in some situations, it does not involve storing data.

Complex event processing assesses events streaming from multiple data sources by looking for patterns and creating real-time insights. CEP allows applications to take proactive steps to tackle problems based on these insights triggered from different sources, such as a diverse system landscape within any organization, various business processes, or multiple business scenarios at any given time. An example of this would be a payment platform that receives thousands of payments per minute during peak hours where a CEP platform could be used to detect fraudulent payments from patterns of purchases. In CEP, Events are processed, mapped, and linked to data objects created to perform specific actions like alerts, automated processes, and triggering workflows. 

Complex event processing is central to the Internet of Things (IoT), and the diverse, interrelated application landscape, transmitting an estimated 500 zettabytes of data per year. The sheer volume of data requires that organizations identify threats and opportunities, further secure their data flows, and streamline their operations as demonstrated by Apache SAMZA, Amazon Kinesis Analytics, and IBM.  Complex event processing facilitates this activity by utilizing low latency with thousands of events per second, and maps out complex events and their patterns along with their spatial relationships.

A front-end application/user interface


Historically, video data has posed a significant challenge for machine interpretation and data extraction. Complex event processing sometimes lacks the required expressive query language but VidCEP provides an event framework to detect spatiotemporal patterns in video streams. VidCEP does this through Deep Neural Network models with graphic base event representations. 

Stream Workers are Macrometa's internal CEP processing engine with serverless tools for knowledge-based accumulation of data, rules, and procedures to generate alerts and actions. Stream Workers allow you to integrate streaming data and take dynamic action based on programmatic rules and real-time machine learning models.

Source:   https://macrometa.com/cep/reference/overview/

The stream processing engine receives the event data and processes it in real-time to extract meaningful information for decisive action.


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Learn more about CEP

Want to know more about the differences between Streaming analytics platforms (like Spark and Flink) vs Complex Event Processing platforms (like Macrometa)? Check out our webinar and learn which use case is right for you.

Macrometa Stream Workers is CEP platform that provides a comprehensive streaming process engine to process rich and complex data in real-time. Learn more about the intuitive programming language that powers Stream Workers. To see Stream Workers in action, we built a full-stack e-commerce web application, in conjunction with Cloudflare, that creates a storefront (and backend) for customers to shop for fictional, not just fiction, books. See the tutorial on GitHub.


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