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Table of contents
Real-Time Data Streams and Complex Event Processing(CEP)
Using Streaming Data
Using Complex Event Processing
Differentiating CEP from Streams 
Frequently Asked Questions
What are the applications of CEP?
What is data streaming?
What are the applications of data streaming?
Last Updated: Mar 27, 2024

Differentiating CEP from Streams

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Before heading into the differentiation of CEP and Streams, let's see what CEP and Streams are.

What is CEP?

Complex event processing [CEP], also known as an event, stream, or event stream processing, is the use of technology for querying data before storing it within a database or, in some cases, without it ever being stored.


Applications of CEP

  • Market Data
  • Sensor Networks
  • Business Activity Monitoring 

What are Streams?

Streaming data(streams) is generated continuously by thousands of data sources(raw data), which typically send in the data records simultaneously, and in small sizes ( basically Kilobytes). Streaming data includes various data such as log files generated by customers using your mobile or web applications, e-commerce purchases, in-game player activity, information from social networks, financial trading floors, telemetry from connected devices, and geospatial services or instrumentation in data centres.

Real-Time Data Streams and Complex Event Processing(CEP)

So what is streaming data, and how is that different from CEP? This is not an easy question because a continuum of data management exists(Well, we will see further in this article).

Streaming computing was designed to handle a continuous stream of thousands of unstructured data. While Complex Event Processing (CEP) typically deals with a few variables that need to be correlated with specific business rules and processes. In many situations or areas, CEP is dependent on data streams. However, CEP is not required for streaming data. The use of both technologies is quite different. Like streaming data, CEP is intended to analyze the streams of data in motion. If data is not in motion, or we can say it is at rest, then it does not fit into the category of CEP or streaming data. The following section discusses streaming data and its application in organizations and industries. 

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Using Streaming Data

Streaming data is an analytic computing platform; this is particularly targeted at speed. These programs require a non-stop circulation of frequently unstructured heaps of facts to be processed. Therefore, data is continuously analyzed and transformed into memory before storing it on a disk.

Some fundamental principles define when to use the streams is most appropriate:

 If you need to identify retail purchase opportunities at engagement, either through authorization-based messaging or social media.

Gather information about your movements in a safe place. 

To be able to respond to events that require an immediate response. 

Loss of service or changes in patient health.

Real-time calculation of costs that depend on usage and available resources.

Using Complex Event Processing

Both data streaming and Complex Event Processing (CEP) are intended to manage the data in motion. But CEP focuses on a few variables, and the uses of these two technologies are quite different. Complex Event Processing(CEP) is a technique for tracking, analyzing, and processing data as an event happens, While streams are intended to diagnose large volumes of data in real-time. This information is then processed and communicated based on processes and rules. The idea behind CEP is to establish the correlation between streams of information and match the resulting pattern with defined behaviours such as seizing an opportunity. It is an advanced approach based on simple event processing that collects and combines data from relevant sources to discover events(as mentioned in the name that processes events) and patterns that can result in action.

Differentiating CEP from Streams 

So, what is the difference between CEP and streaming data solutions? 

CEP Streams
CEP engines support high-level operators and provide you with high-level languages. Stream Processing Engines force you to write code, and often they do not have higher-level operators such as windows, joins, and temporal patterns.
CEP engines have centralized architecture, typically having two or a few nodes. Stream Processing Engines are parallel by design. They support large 10-100s node computations.
CEP engines often receive and process the data in memory, and when a failure happens, they often choose to throw away failed events and continue. Stream Processing engines stress reliable message processing, often consuming data from a queue such as Kafka. 
CEP engines are tuned for low latency. Often they respond within a few milliseconds of latency. Whereas most the Stream processing engines take close to a second to generate results.
CEP, on the other hand, will not manage as much data, so it is often run on less complex hardware Streaming data applications manage a vast amount of data and process it at a high rate of speed. Why? Because of the amount of data processed, it is typically managed in a highly distributed clustered environment.


Frequently Asked Questions

What are the applications of CEP?

CEP enables applications and systems to respond to events, trends, and patterns in the data as they happen. It is typically more associated with searching for complex patterns and dependencies in the incoming data.

What is data streaming?

Streaming data is the continuous flow of data(also known as streams). A data stream consists of a series of data elements ordered in time.

What are the applications of data streaming?

Location data.

Fraud detection.

Real-time stock trades.

Marketing, sales, and business analytics.

Customer/user activity.


Thus we can conclude that Stream processing or Event Stream Processing is one of the ways of realizing CEP. Other approaches to building CEP systems include Rule-based, State-based (like EventZero), Neural network-based, and possibly others.

The good news is that both CEP and Stream Processing technologies are merging, diminishing the differences. Both can learn from the other, where event processing needs high-level languages and lower latencies while CEP needs to scale and process events reliably. 

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