What are Real-Time Streaming Applications, and Why Do They Matter?
Whether database, distributed, web, or mobile, all applications are built around a core set of underlying technological capabilities. Notably absent from this list is the ability to build real-time streaming applications.
Every enterprise that leverages streaming data for analytics would likely want to use that same data in other ways to enhance the user experience. Enterprises are streaming massive amounts of data, but few use that data to its full potential.
Even though streaming data has become extremely commonplace, streaming applications have yet to be heavily adopted. Streaming applications allow organizations to utilize data in more ways and unlock new business.
What are Streaming Applications? Streaming applications are different from stream processing and streaming analytics. Stream processing is the processing or computing of data as it is produced or received. Streaming analytics, on the other hand, refers to the ability to analyze incoming data as it arrives and perform pre-defined computations, such as those needed for Business Intelligence dashboards.
Streaming applications need more than analytics: They also need the intelligence residing in the business logic. To provide real-time insights, streaming applications must know their own state at every moment because any moment can be an expiring opportunity. Streaming applications must have all relevant state ready without needing to ask a database.
There are fundamental differences between real-time streaming and legacy applications. In real-time streaming applications, entity model updates are continuously pushed and propagated in sync with the data stream. Thanks to Streaming APIs, the business logic continually executes in a real-time state instead of responding to queries. Polling is no longer necessary, removing inefficiencies, and users do not need to request data that is automatically being streamed to their UI as soon as it is available. For streaming applications, UIs are universally event-driven, whereas in traditional applications, they are usually half-driven by user events and half-driven by polled backend queries.
The Importance of Streaming Applications As consumers continue to demand real-time, personalized experiences, streaming applications will only grow in popularity. Real-time streaming applications are a practical way to accelerate business development. These applications enable organizations to explore new features, pursue new growth opportunities, and establish a solid competitive differentiation.
Streaming Application Use Cases Streaming applications enable true real-time observability, live modeling of complex business operations, and responsive decision automation at scale. All of these cases can be used to streamline internal workflows and increase customer satisfaction.
For instance, a major telecommunications service provider uses Swim to track network outages in real time. With Swim, this network can offer in-the-moment information to field service crews, IT departments, and customer service representatives, allowing them to directly address customer needs as they arise.
Getting Started with Real-Time Streaming Applications Looking for an easy way to build real-time applications directly on top of streaming data? Swim is the first (and only) open-source, full-stack platform for building, managing, and operating streaming applications at scale.
Building a real-time streaming application is a straightforward process. To start, you need a real-time entity model. This will allow the application to execute general-purpose business logic in real time. It will also allow you to leverage two other components: a streaming application programming interface and a real-time user interface framework.
The Swim platform is essentially a library: instead of working from some cookie-cutter model, developers can take exactly and only what they need. For example, an engineer can employ their existing frameworks and add Swim for the real-time state, which would likely come into Swim the same way they would come into the application, such as Kafka. From there, business logic can be written directly on top of the event stream, and the back-end code could use the Swim client library to receive any required state updates in the controller (assuming Java is being used). Meanwhile, logic can be directly reflected in the UI using Swim’s client library for the web browser.
Web Agents continuously analyze, learn and predict both their own states and the states of the other Web Agents they are linked to. They make it possible to connect the dots between multiple real-time data streams, delivering timely insights and informing better business decisions.