Data is everything in this modern era. Businesses can use it to inform better decisions, personalize experiences, and generate better outcomes for organizations and their customers alike. Of course, data is only as valuable as the insights taken from it. That’s where event-based streaming comes in.
What is event-based streaming?
Data on its own has a wealth of potential. Yet, without collecting, parsing, and reading the results, it remains simply that: potential. Event streaming technologies bridge the gap between data and insight while eliminating bottlenecks to generate real-time outcomes.
How does event-based streaming work?
To start, it’s crucial to understand what an event is: a change in the state of an object due to an action, such as an end-user activity or automated transaction. Each event contains uniquely relevant data; businesses often want to track these moments for analysis or record in real time.
Data event streaming meets the on-demand needs of organizations and end users by sending this data for processing as events occur. In an event streaming architecture, data moves in a continuous flow – whenever events trigger the action – along with necessary information on state changes.
Data can be generated from various sources, including apps, devices, sensors, databases, cloud services, and more. Processing platforms will ingest and store this data for future access, allowing organizations to gain real-time insights and react retrospectively to occurrences.
Stream processing vs. batch processing
With batch processing, data is ingested and held to be processed either at predefined intervals or with a manual request. While batch processing has benefited businesses for many years, it’s time to clear the way for stream processing tools.
Stream processing increases efficiency and the availability of data insights by continually processing available data points. Companies with large volumes of data benefit from event-based processing, both saving time and reducing the need for large amounts of hardware otherwise required to hold batches.
Practical applications of event streaming
There are numerous applications for event stream processing, some of which include:
Tracking
Tracking order processing stages, including reservations and retail shopping (eCommerce and brick-and-mortar). Organizations benefit from tracking their user journey and analyzing real-time booking and purchasing data to update inventory levels.
Financial
Financial transactions, including payment processing, batch payments, and refunds. Not only are accurate financial records crucial, but tracking and analyzing payment trends can help inform business decisions.
Logistics
Logistics tracking for both fleet and parcel status aids in providing real-time information to customers and optimizing the supply chain.
Sensor Data
IoT and sensor data for interpreting and improving upon machine learning and AI models. On-demand review and reaction is crucial to these models, which provide real-world data, and often have many data points.
Internal Data
Internal data such as system, user, or changes to the IT environment. This is particularly valuable for fraud detection and for tracking related to compliance and governance requirements.
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Event-based streaming will ingest data in any of these scenarios to generate analytics to identify patterns and trends, and alerts based on actionable insights. The approach can also be used to monitor system performance, track project statuses, and provide feedback. Event streaming is a useful approach for any organization with continuous data streams that could benefit from real-time insights generated from that data.
