Event stream processing is a concept often used in the field of IT. For example, take a system that continuously creates data. Each data point in that system represents an event. A continuous stream of those data points, or events, is referred to as an event stream. Developers commonly refer to event streams as data streams though, because event streams consist of continuous data points.
So, now that we understand event streams, what is event stream processing? This article answers that question and discusses how event stream processing works, why you should use it, and the benefits of event stream processing.
What Is Event Stream Processing?
Event stream processing refers to taking action on the generated events. There are many different ways to take action on events. Here’s are some examples:
- Performing calculations, such as mean, sum, or averages
- Transforming data, such as changing the format of a number or text field
- Analyzing data, such as predicting future system behavior based on patterns in the data
- Enriching data, such as adding more information or metadata to events
Let’s say we are streaming bank transaction events. Each event represents a financial transaction. However, your application receives those events as plaintext. Therefore, we first want to transform the plain text event into a JSON object. Next, we want to enrich the bank transaction event with metadata, such as the current date. This information will be useful when we store the event in our database later.
As you can see, it’s possible to create a pipeline of actions to transform event data. This is exactly what event stream processing is all about.
How Does It Work?
Event stream processing often encompasses two types of technologies. The first is a system that stores events in chronological order and the second is software to process events. Both of these technologies are often incorporated in the same tool.
Most commonly, developers use Apache Kafka to store events temporarily and process them. You can classify Apache Kafka as a stream processor or stream processing engine. Apache Kafka lets you define different event streams and take different actions on them. Furthermore, you can build event stream pipelines where you pass a processed event to another event stream for further processing.
Apache Kafka supports the following aspects of event stream processing:
- Publishing (writing) and subscribing to (reading) event streams
- Storing streams of events reliably without time constraints
- Processing streams of events as they occur
Why Should You Use Event Stream Processing?
Event stream processing is useful when you need to take immediate action on a data stream. Therefore, you can equate event stream processing with real-time processing.
Event stream processing matters most for high-speed technologies that are popular nowadays. For example, let’s assume again that you have to process financial transactions. You want to detect malicious behavior such as payment fraud or money laundering. Event stream processing allows you to run fraud detection algorithms faster than a card swipe, detecting fraudulent activities in real-time. Therefore, your business can focus on scaling payment processing instead of fraud detection.
In other words, event stream processing handles large amounts of data in time-critical environments. Furthermore, some companies opt for event stream processing technology because their business intelligence tool doesn’t offer the advanced logic they need or just can’t handle such large amounts of data. Data is just arriving too fast for a conventional business intelligence tool to take care of it. Therefore, event stream processing is the go-to solution.
How Is Event Stream Processing Different From Batch Processing?
First of all, companies are dealing with much larger amounts of data than they used to. Therefore, we require more advanced data processing tools. A traditional application would intake data, store the data, process the data, and finally store the processed result or send the result to another tool.
These processes happen in batches. Your application waits until it has enough data—a batch—before it starts processing the data. For example, imagine that your application receives 100 data points every minute. Your application might wait until it has 1000 data points before processing any data. In other words, you have to wait at least 10 minutes for the data processing to start. This is unacceptable for real-time or time-critical applications that require immediate data processing.
Event stream processing works totally differently. Each single data point or event gets processed immediately. Therefore, there’s no queue of data points at all.
This article by Srinath Perera explains further why event stream processing is a better choice than batch processing. “Sometimes data is huge and it is not even possible to store it. Stream processing lets you handle large fire horse style data and retain only useful bits.” This is a very valid argument. The amount of data will only continue to grow with a rapidly expanding IoT market like we have today.
What Are the Benefits?
Here’s a list of benefits you get from event stream processing.
- It offers the ability to build event stream pipelines to serve advanced streaming use cases. For example, if you want to first enrich event data with metadata and then transform the data object into a JSON object for storage, you can use an event stream pipeline.
- It processes and analyzes large amounts of data in real-time, giving you the ability to filter, categorize, aggregate, or cleanse data before storing.
- It scales your infrastructure seamlessly when data volume increases.
- It enables continuous event monitoring, which allows you to create alerts to detect patterns or anomalies.
- It allows for real-time decision-making.
The next section explores when you should use event stream processing.
When To Use Event Stream Processing
The simplest answer to this question is to use event stream processing whenever you need to handle large amounts of continuous data. However, event stream processing is most useful when you want to leverage its real-time nature. In other words, when you want to take immediate action on events.
People, sensors, and machines generate most of our data. As IoT continues to evolve, more and more data comes from sensors and machines.
You’ll often find event stream processing among the following industries:
- Fraud detection
- Financial industry, especially the banking industry
- Intelligence and surveillance
However, the use of event stream processing is not limited to these industries. You might be surprised where you find event stream processing technology. For example, the New York Times uses Apache Kafka to store and distribute published content in real-time to various applications and systems that make the content available to readers.
That’s what event stream processing is all about. I hope you now understand that event stream processing matters most for time-critical applications that deal with a large number of continuous data points in real-time. It’s a great alternative to traditional batch processing, which doesn’t allow for real-time processing.
Want to learn more about event stream processing? Take a look at the event data cloud developed by Scalyr. Their white paper introduces the concept of an event data cloud as an alternative to traditional event stream processing. Traditionally, event data is only used for processing purposes. However, you can derive much more information and many metrics from event stream data. For example, you can monitor usage patterns for your application through event stream processing.
Therefore, real-time event data can tell us much more than batch-processed data. When event streams are processed in real time we get a granular view into the health and performance of digital systems and services, as well as insight into security issues, performance trends, usage patterns, user needs, optimization, and so forth. Traditional event stream processing tools don’t offer these services, but they are exactly what Scalyr’s event data cloud offers.
This post was written by Michiel Mulders. Michiel is a passionate blockchain developer who loves writing technical content. Besides that, he loves learning about marketing, UX psychology, and entrepreneurship. When he’s not writing, he’s probably enjoying a Belgian beer!