DEVOPSdigest

Building pipelines that can share data in real-time instead of batch across enterprise IT systems can drive faster and better innovation.

Equipped with data that flows, like fresh water may flow, seamlessly between different applications and systems, developers can more easily come up with solutions that boost productivity, accelerate decision-making, enable better integrations, and more.

This blog will explore why and how organizations should prioritize breaking down silos and moving to real-time data to turbocharge developers' capabilities.

Empowering Developers with Real-Time Data Streaming

Real-time data streaming has become a priority for organizations recently — and for good reason.

For the past 10 years, organizations have been modernizing their systems to Event Driven Applications (EDAs). This change has seen applications be broken down into smaller microservices that work together by sharing real-time information that's available in a "stream."

To paint a picture: think of a house without plumbing. If you lived there, you'd have to go fetch water from a nearby lake every day. You'd need a bucket, would have to spend time and energy getting the water each day, and, when you eventually got home, the water wouldn't be fresh.

For obvious reasons, it'd be better to modernize the house with plumbing.

In this example, the house is an application or microservice being built and the water is data.
This architecture, popularized by streaming infrastructure like Apache Kafka, has two main benefits.

First, it makes it easier for engineering teams to build new applications that consume or react to data since it is provided constantly.

Second, the data is available in real-time, so engineering teams can build applications that are more responsive and efficient to specific needs.

For example, a fraud prevention application needs real-time data to do its job.

This shift has empowered developers to build and integrate innovative solutions more effectively than ever before while, at the same time, liberating data to be consumed by downstream applications.

Turbocharging Developers - Turning Data Streams Into Engines of Innovation

It's been said that AI is only as good as the data that fuels it.

While this is true, it must also be said that data is only as valuable as its accessibility.

Developers are the driving force within organizations for transforming raw, unstructured data into meaningful and actionable applications. Unfortunately, many organizations still operate within silos where data is not accessible.

By breaking down silos with real-time data streams, organizations can empower developers to build innovative solutions to unique business challenges.

Customer service provides a great example of this in action.

Think of call centers that deal with unhappy customers all day. With access to real-time data, an engineer could build multiple solutions that work together seamlessly, including:

■ An AI tool that analyzes customers' tone of voice to alert the operator to what a customer service agent can expect when they get on the line.

■ An app that calculates a discount or incentive to offer the customer to prevent churn.

With these applications, built independently but working in concert, the call center can significantly improve outcomes.

All of this is facilitated by real-time data streaming.

Democratizing AI Agents and the Next Wave of Applications

AI agents are increasingly being used in tandem to deliver impressive results, operating like smaller applications that rely on a shared real-time data stream.

Many organizations find AI agents to be more reliable, predictable, and easier to manage and scale when broken down into smaller and more focused roles. These agents, which operate autonomously, are trusted to collaborate with other agents, applications, and systems to maximize efficiency and alignment.

And these AI agents can be developed in minutes with just a few lines of natural language pointing to an LLM. This can be done by anyone within an organization — no engineering experience is required — and without thousands of lines of code.

But to truly empower anyone within an organization to act as an engineer and get the most out of an AI agent developed this way, real-time data streaming is essential.