Axual Documentation

Another key benefit of data streaming is its efficiency in reusing data. In traditional systems, APIs or database queries might be repeatedly called to fetch the same information, leading to inefficiencies and delays. Streaming data ensures that the data is accessible to multiple consumers at the same time, allowing different applications to use it as soon as it’s generated without repeated requests. This asynchronous communication not only speeds up the process but also reduces the load on systems.

In a streaming architecture, data producers (like sensors or applications) generate events and push them to the platform, without needing to know who will use the data.

Similarly, consumers (applications or services) can subscribe to data streams they’re interested in and react to those events in real-time.

This decoupling of producers and consumers creates a more flexible system, where data can be used by multiple services without requiring tight integration, as is often necessary in traditional batch systems.

Producers might not care about who is reading their messages; they need to speak in a language understood by the other party, the consumers.

In other words, the messages they produce to a data stream need to adhere to a specific schema, comparable to an API specification or a database table structure.

You might conclude that there isn’t a decoupling between producer and consumer strictly speaking because they need to agree on what schema to use for messages on a particular data stream. This is only partly true; producers are allowed to change schemas considering the backward compatibility of the schemas.

Data streaming platforms are designed to handle the high velocity and volume of real-time data, offering scalability and stability.

Unlike batch systems that might struggle with spikes in data or system failures, streaming platforms ensure that data is always available and that systems can continue to operate without disruption.

Streaming data naturally supports reactive patterns, where systems respond to events as they happen. This flexibility allows consumers to process events at their own pace, a contrast to the rigid structure of batch processing. As a result, streaming data can often replace traditional request-response communication, creating more efficient, event-driven systems.

In summary, while older approaches like batch processing have their place, data streaming is crucial for modern systems that require real-time, scalable, and flexible data processing. It allows organizations to act instantly on critical data, making their operations more responsive, efficient, and future-proof.