An event stream processing platform for businesses works to deliver on the requirements of a digital business, letting you;
- Collect data from multiple sources
- Understand the context and meaning of this data
- Recognize and act swiftly on key business moments.
What is an Event Processing Platform used for?
Event Processing Platform
An event-processing platform is a computing platform that performs operations on events as they report in a system by observing or listening to an event from the holistic environment. The common information/data processing operations include creating, reading, and processing events.
While on the other hand, as explained before;
Event Stream Processing
Revolves around the onboarding, integrating, analyzing, and simplifying the unceasing streams of information or data – In ways that offer insights to users of the technology, preferably closer to real-time as much as possible.
The Common Ground
Stream processing can also be known as event processing, complex event processing, real-time analytics, or streaming analytics.
Explaining Event Stream Processing
It is a practice of taking actionable decisions on a series of data points that originate from a system that continuously generates data.
The term event refers to every data point in the system, and the stream is the continuous delivery of such events. A series of events are also referred to as streaming data or data streams.
Actions that can be taken for these events
- Aggregations – calculations like sum, mean, standard deviation
- Analytics – such as predicting future events based on data patterns
- Transformations – changing numbers into a date format
- Enrichment – fusing data points with other data sources to generate amplified meaning and context.
- Ingestion – insertion of data in a database.
Event Stream Processing – An Overview
The event stream processing is often seen as complementary to batch processing.
Batch processing takes action on a massive set of static data – data at rest, while the event stream processing takes action on the constant flow of data – data in motion.
Stream processing becomes important when action needs to be taken immediately. Event stream processing environments are described as “real-time processing.”
Additionally, there are many related, and synonymous terms for event stream processing, like the stream processing is also considered an equivalent term. Complex event processing – CEP, is also termed as mostly identical – however, it implies an older class of technology in contrast to the newer, high-speed technologies that are the rage today.
The term message is also used to refer to events; thus, the holistic system can be called a messaging system.
How Event Stream Processing Works in real-time?
The event stream processing handles data sets with one data point at a time.
Instead of viewing data as a whole set, the event stream processing deals with a flow of continuously flowing data. This, however, needs a specialized set of technologies.
Technology Classes for Event Stream Processing
For event stream processing, there are two major classes of technology;
- System that stores events
- Technology helps developers write applications that take action on events
The prior component relates to data storage and stores data based on a timestamp.
For instance, you might want to capture outside temperature every minute of the day and treat it as an event stream. In such case, each event is the measured temperature next to the exact measurement time.
The latter – commonly known as the stream processors or the stream processing engine, is the true event stream processing component, enabling you to take action on the incoming data. A range of processor options are available in the market today.
While most appear similar in capabilities, the in-memory stream processors stand out from the rest owing to their unique ability to swiftly process a massive amount of streaming data.
Example Use Cases of Event Processing Platform
Uses cases for Event Stream Processing like the;
- Payment Processing
- Anomaly Detection
- Fraud Detection
- Predictive Maintenance
- IoT Analytics
All of these rely on immediate action on the information/data streamed.
These use cases deal with data points in a continuous stream – each is associated with a specific point in time. All of these are conventional event stream processing examples – as the timing and order of the data points aid with identifying patterns and trends representing important insight for users.
Event stream processing is more important when data granularity becomes critical. When the actual changes to stock prices are more important to a trader than the stock price in itself – event stream processing help to track all changes on the way to making better decisions.
The change of data – CDC, practice in which all individual modifications to the database are tracked, in yet another event stream processing use case. In CDC, the downstream systems use the stream of individual updates to the database for purposes of identifying use patterns.
Thus helping define optimization strategies and tracking changes for audit requirements.
Final Thoughts
There is no doubt that event processing platforms have truly become the need of the hours for business in the modern world. Get in touch with Memphis{dev} for the best event processing platform and event stream processing solutions.