Until recently, most enterprises treated real-time data as something you reached for only when absolutely necessary. It sat at the edges of enterprise architecture. However, that has changed. If you have been following our coverage at BigDATAwire, you would have seen that real-time data and analytics has been a core theme for enterprise modernization efforts and data infrastructure announcements.
Confluent and Databricks joined forces last year to bridge AI’s data gap. The partnership is framed around data-in-motion layer for AI pipelines. Snowflake and Ataccama also partnered on real-time data trust for enterprise AI.
Real-time is also a core theme across scientific AI space. Rather than analyzing batch data later, researchers are now leaning heavily toward analyzing data as it comes in. Brookhaven National Laboratory has embedded AI into physics pipelines as data volumes explode. Berkeley Lab has linked detectors to supercomputers for real-time nuclear physics analysis.
Across both enterprise and science, the common thread is the same. Data is moving closer to the moment decisions are made, whether those decisions are made by humans, software, or AI systems.
Why Real-Time Moved From Edge Case To Default?
To better understand this shift, let’s rewind a bit. What exactly did real-time mean when it was used as an edge-case? Well, historically “real-time” was only used when absolutely necessary. The use cases were mostly tied to a report, not an action.
So the goal was more about understanding, and less about reacting. It was considered risky because it was hard to know if data was complete and reliable. If any issues emerged, debugging in real-time was painful. It was another system that could fall behind or malfunction. Most enterprises limited real-time to high-stakes use cases only, such as fraud detection or network monitoring.
(Sutthiphong Chandaeng/Shutterstock)
The equation has changed since then. One of the reasons for that is that cloud platforms and managed streaming services removed much of the operational burden that made real-time feel risky. While costs still remain a major concern, it has become significantly easier (and more predictable) to get an idea to a working production pipeline. Enterprise systems don’t have to wait for the next scheduled run to get the data they need.
Another important thing has been the combination of AI and automation pulling data closer to execution. By embedding models directly into workflows, enterprises can now get fresh data. It can also expose latency issues that were previously hidden behind reports and dashboards.
When AI systems feed on bad or stale data, it shows in the quality of the output. This can translate to bad business decisions. Now we see modern applications generate continuous streams of events from various sources, such as customer behavior or system telemetry. The market has moved from periodic snapshots to more continuous signals as data comes in live. This is how real-time is now close to the architectural core, and not an edge case.
It’s worth keeping in mind that real-time becoming the default does not mean every system needs millisecond updates or always-on streaming, but it does mean teams increasingly assume fresh state should be available when decisions are made.
How Enterprises Are Applying Real-Time Today
Most enterprises are using real-time as an execution layer. It has moved beyond simply being a reporting layer. This means data gets used by systems and workflows first, and then can be summarized to be used in dashboards or reports as needed. Many systems are now designed to take actions directly through real-time streams. For example, applications can now adjust system behavior or update recommendations as events are still unfolding.
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In many environments, real-time data is used to maintain a continuously updated state. This could be session context, inventory levels, risk scores, or system health indicators. Any type of data that helps applications reference as they make decisions.
What has changed is how teams design around this state. Instead of building pipelines that exist purely to feed analytics, data teams are building systems that expect state to be fresh by default. This affects how services communicate, how failures are handled, and how workflows are structured. Real-time data is often used to gate actions, enforce constraints, or prevent systems from drifting out of sync as conditions change. Over time, this shifts the role of analytics downstream. No doubt that insight still matters, but it increasingly follows execution rather than leading it.
The Same Shift Playing Out in Scientific Data Pipelines
A similar shift is happening outside the enterprise. Data arrives at a scale and speed that also challenges traditional batch workflows in scientific research. The store everything, and then analyze later approach doesn’t work anymore. Real-time processing is often used to filter, tag, or rank incoming data so that only the most relevant signals are pushed downstream. This helps reduce storage pressure, as well as wasted compute on low-value or incorrect data.
With more real-time feedback, the researchers are also able to adjust the experiment or study as needed. This may include adjusting parameters or experimental conditions after detecting anomalies from the incoming data. They might even adjust the settings just to test different conditions or ideas.
(Inovational World/Shutterstock)
In many facilities, this is enabled by linking instruments and detectors directly to high-performance computing resources. Instead of collecting data first and sending it off for analysis later, computation runs alongside data collection. This means no waiting for a separate post-processing phase to begin – you get to see results as experiments are still underway.
Across life sciences and imaging-heavy research, real-time analysis is being used to triage incoming data, deciding what should be kept, what can be compressed, and what can be discarded immediately to manage storage and compute limits.
Even as real-time becomes more common, it introduces new challenges. Reliability is one of the biggest. As real-time systems run continuously, there might not be enough oversight to catch issues. Even minor issues that slip through the cracks may escalate quickly. The nature of real-time systems is also costly. Any spike in data or traffic can add to the infrastructure spend – not something enterprises want to do, especially as they are already concerned about ROI delivered by their AI initiatives.
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The post Why Real-Time Became the Default — and How Data Teams Are Actually Using It appeared first on BigDATAwire.
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Author: Ali Azhar
