In a data-driven world, stream processing offers firms a path to real competitive advantage

When it comes to running a successful business, gaining competitive advantage is everything. That said, pinpointing exactly what might give firms that ‘edge’ isn’t an exact science. It might be a new product or service. It may reside within the creativity or dedication of employees. Or it may simply be down to luck or being in the right place at the right time.

But in today’s data-driven world, gaining a competitive advantage can also be sought in the back-end systems that power digital processes and operations.

The argument in favor of this approach is simple. The more data you can process in a shorter time frame, the greater the chance you have to gain that all-important edge against your rivals.

Increasingly, this means stream processing — a type of data processing that deals with continuous, real-time data flows rather than traditional stop/start batch processing.

Batch processing is fine if time isn’t an issue or if something can wait until later. But for those organizations that need to act on information now, waiting a few minutes — or even a few seconds — is simply unacceptable. They need to be able to access data that is current, free-flowing and up-to-date. Put another way, they need ‘data in motion’ rather than data that’s ‘past its sell-by date’.

The financial world revolves around fast data

Take finance, for example. It’s estimated that people around the world make more than two billion digital payments each day. Simply managing those transactions is a gargantuan task. But trying to detect and combat fraud amongst all the millions of legitimate payments is on another level.

For banks, credit card companies and other financial institutions, being able to stop fraud in the seconds before a transaction is completed would save millions and go some way to help putting fraudsters out of business.

The same is true for retailers trying to spot fraudulent purchases. Being able to identify and block a fraudulent transaction at the point of sale — either in-store or online — would help retailers in their fight against criminal gangs and one-off chancers.

Of course, many of those checks are already being made. And increasingly, they’re being made thanks to data stream processing.

For the IT teams behind the scenes, these payments aren’t necessarily seen as ‘transactions’ but as ‘events’. To them, every transaction is made up of many different ‘events’.

Millions of transactions per second translate into billions of ‘events’ — all of which have to be processed in real-time. And this can only be done effectively using event-driven architecture.

The case for event-driven architecture

Perhaps that is why so many developers use technology rooted in the Apache Software Foundation (ASF), an organization widely recognized and respected within the open-source community and among software developers.

Its community-driven approach to software development has resonated with developers, businesses, and organizations worldwide, making Apache projects a cornerstone of the open-source ecosystem.

For event-driven architecture — the kind of system essential for stream processing — you need two key elements: storage and processing. And Apache has both of them covered. 

Apache Kafka is a distributed streaming storage platform used for building real-time data pipelines and streaming applications. It has become a key technology for handling event-driven architectures.

Apache Flink, on the other hand, is an open-source stream processing framework and distributed processing engine.

Kafka and Flink are go-to software for developers

Together, they have become the de facto chosen architecture by developers for the streaming pipeline giving businesses and organizations a real competitive advantage.

But the success of this combination — and the source of this competitive advantage — isn’t just down to speed. It’s also down to firms to make sense of the data they’re given, how they interpret the information and how they act.

In a sense, it’s not too dissimilar to the way artificial intelligence (AI) works. As we've seen from the explosion in interest in tools such as ChatGPT, AI models are only as good as the data they use.

Traditional AI models tend to ‘learn’ from data that is static. As such, it can only provide information or responses based on that historical static data.

But an AI model that can ‘learn’ from data that is constantly being updated can generate responses that reflect changes. That’s why stream processing is such a game-changer.

Stream processing is the future for AI models

For those seeking data-driven competitive advantage, it makes sense to use the most up-to-date information available. After all, the world is changing every second. Why shouldn't data reflect that?

Imagine two retail sites each running an AI-supported product recommendation service. Both plug products that are based on the weather.

The first uses batch data generated overnight. The other uses data that is constantly updated by the country’s leading meteorologists.

Visitors to the first website may receive a recommendation to buy an umbrella because, 24 hours ago, forecasters predicted heavy rain.

The other website offers a range of sunscreen to suit all pockets because an overnight shift in weather patterns now means wall-to-wall sunshine.

That’s the advantage of stream processing. That’s the power of stream processing. It allows businesses to make decisions based on the most accurate and up-to-date information available to provide that all-important competitive edge. And that’s why more and more companies are turning to stream processing to give them that competitive edge.