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Table of contents
1.
Introduction
2.
Example of CEP
3.
Applications of CEP
4.
Difference Between CEP and Streams
5.
Understanding the Impact of Streaming Data and CEP on Business
6.
FAQs
7.
Conclusion
Last Updated: Mar 27, 2024
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Using Complex Event Processing

Author soham Medewar
0 upvote
Master Python: Predicting weather forecasts
Speaker
Ashwin Goyal
Product Manager @

Introduction

Streams and Complex Event Processing (CEP) are both designed to manage data in motion. However, the applications of these two technologies are considerably different. Unlike streams, which are designed to evaluate massive amounts of data in real-time, Complex Event Processing is a mechanism for tracking, evaluating, and processing data as an event occurs. This data is subsequently processed and transmitted in accordance with corporate rules and processes. CEP is based on the ability to develop a correlation between streams of information and match the resulting pattern with specific behaviors such as reducing a threat or seizing an opportunity.

CEP is a sophisticated approach based on simple event processing that collects and combines data from several relevant sources to uncover events and patterns that can lead to action.

Example of CEP

Here's an illustration. To encourage recurring purchases, a retail chain introduces a tiered loyalty program, especially for consumers who spend more than $1,000 each year. It is crucial that the organization develops a platform that will allow these critical clients to return and spend more money. When a high-valued client utilizes the loyalty program, the CEP platform activates a process that provides the customer with an additional discount on a connected product. Another process rule could surprise the consumer with a bonus discount or a free trial of a new product.

 

The corporation also introduced a new loyalty program that is linked to a mobile app. A text message offers a discounted price to a loyal customer walking near a retailer. Simultaneously, if that devoted consumer posts something unpleasant on social media, the customer service department is notified, and an apology is sent. It's very likely that we're working with a large number of clients who have numerous interactions with us. However, simply streaming and analyzing data would not be sufficient. To fulfill the retailer's business objectives, it would be necessary to implement a method to respond to the study's findings.

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Applications of CEP

CEP is used by a variety of sectors. CEP is used by credit card firms to better monitor fraud. When a pattern of fraud is discovered, the company can disable the credit card before it suffers major damages. The underlying system will correlate incoming transactions, track event data, and initiate a procedure. Financial trading apps, weather reporting applications, and sales management software all use CEP. What all of these applications have in common is that they all have a set temperature, pressure, transaction size, or selling value norm.

 

CEP solutions are available from a variety of suppliers. The building of real-time, event-driven applications is possible using many of the CEP solutions on the market. These applications can ingest data from streams as well as data from standard database sources. The majority of the solutions have common features such as an Eclipse-based graphical programming environment, access to real-time data flows, and APIs to historical data sources.

 

The majority of these tools support SQL and have a graphical event flow language. Esper (an open-source provider), IBM's IBM Operational Decision Manager, Informatica's RulePoint, Oracle's Complex Event Processing Solution, Microsoft's StreamInsights, SAS DataFlux Event Stream Processing Engine, and Streambase's CEP are all key players in this field.

Difference Between CEP and Streams

Unlike stream computing, which is used to analyze large amounts of data in real-time, CEP is focused on solving a specific use case based on events and actions. A streaming data approach, on the other hand, is frequently utilized as a part of a CEP application. Streaming data applications often handle a large amount of data and process it quickly. Because of the volume of data, it is usually managed in a clustered, highly distributed system.

 

CEP, on the other hand, usually doesn't deal with as much data; hence it's usually performed on simpler hardware. Furthermore, the type of analysis will differ. Connectivity to essential systems of record, such as customer relationship management (CRM) systems or transaction management environments, is critical for CEP applications. CEP environments frequently deal with only a few variables that are applied to extremely complicated models and processes. Rather than depending on complicated mining or statistical models, CEP systems are built around a rules engine, which triggers an action when an event occurs.

Understanding the Impact of Streaming Data and CEP on Business

Both streaming data and CEP have a significant impact on how firms may use big data strategically. Companies may process and analyze data in real-time using streaming data to acquire quick insight. Continued analysis of crucial results that may have gone missing in the past often necessitates a two-step process. Companies can use CEP methodologies to stream data and then apply business rules to the outputs of that streaming data analysis using a business process engine. The core benefit of streaming data techniques is the ability to generate insights that lead to new innovation and action.

FAQs

1. What is CEP used for?

Complex event processing (CEP) is a system for gathering, processing, and analyzing huge amounts of data in order to derive real-time insights from events as they happen, similar to event stream processing.

 

2. How does complex event processing work?

Complex event processing is a real-time organizational tool that helps to collect a large amount of data and identify and evaluate cause-and-effect links between occurrences. CEP compares continuously arriving events to a pattern and delivers information about what's going on.
 

3. What is a CEP engine?

Event correlation engines (event correlators) examine a large number of events, identifying the most important ones, and triggering actions. However, the vast majority of them do not result in new inferred events. Rather, they connect high-level and low-level events.

Conclusion

In this article we have discussed complex event processing and its applications. Also, the difference between CEP and streams.

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