Expectations Have Changed—Permanently
We live in a world that expects everything now. People track their parcels obsessively. They refresh flight apps every five minutes to check for gate changes. They want instant payment confirmations, real-time fraud checks, and status updates before they even think to ask. The modern customer is impatient—not because they’re difficult, but because the world has trained them that speed equals competence.
Now flip the perspective. You’re the business. You’re responsible for delivering that seamless, real-time experience. But behind the scenes, your data infrastructure still leans on batch processing—an architecture that was never designed for the velocity and complexity of today’s world.
Delays Aren’t Just Inconvenient—They’re Expensive
When decisions are delayed by hours, sometimes even days, the risks compound quickly. Fraud detection systems that only run overnight can leave your business exposed during peak hours. Imagine a fraudulent campaign that hits at 6 p.m., but your checks only kick in at midnight. By the time your system identifies the anomaly, the damage is done.
A well-known case in e-commerce illustrates the cost vividly. A mid-sized retailer processing around 500,000 orders per month experienced a payment processing outage. Fraudulent transactions slipped through during a brief period of degraded detection logic. By the time the nightly job ran, flagged the transactions, and produced a report, nearly $25,000 had vanished—irretrievable and reputation-damaging.
When Batch Becomes a Liability
This is not just a theoretical issue. In July 2024, the now-infamous CrowdStrike software failure caused outages across global hospitals, airlines, and logistics firms. Systems that relied on nightly data loads or daily refresh schedules couldn’t respond fast enough. Patients were left waiting, flights were delayed, and businesses stood still. That one incident revealed just how fragile batch-driven operations can be when real-time resilience is absent.
The problem isn’t just fraud. In manufacturing, even brief downtimes cost millions. A UK-based industrial firm reported losses exceeding £2 million after a predictive maintenance alert was delayed due to a misaligned batch sync. The alert had technically fired—but it was trapped in a queue waiting for the next scheduled ETL cycle. By the time someone saw it, the damage had already cascaded.
These aren’t just glitches. They are structural failures rooted in a mindset that believes data can wait.
Kafka and Flink: Built for the Moment
But here’s the truth: real-time data doesn’t wait for your batch job. And neither do your customers.
In contrast, companies that have embraced streaming architectures—particularly those powered by Apache Kafka and Apache Flink—operate differently. Kafka acts as a high-throughput, low-latency event backbone, ensuring that data is captured the moment it happens. Flink sits on top, processing that stream in real time, adding context, maintaining state, applying complex logic—all with sub-second responsiveness.
This isn’t about being fancy. It’s about being functional in the now.
From Reactive to Proactive
Consider fraud detection again. Instead of evaluating a transaction after it’s processed, a stream processor evaluates it as it enters the system—enriching it with context like geolocation, device history, and prior behavior in milliseconds. You don’t have to flag it later. You can block it instantly—or approve it confidently.
The same applies to parcel routing, user recommendations, anomaly detection, or monitoring connected devices in the field. In each of these cases, acting on stale data isn’t just inefficient—it’s negligent. And customers, regulators, and stakeholders increasingly view it that way.
Don’t Fly Blind
You wouldn’t guide airplanes using last night’s radar data. So why would you authorize payments, approve insurance claims, or route shipping containers based on outdated snapshots?
Businesses that want to stay competitive—especially in sectors like finance, logistics, travel, and retail—need to think beyond dashboards and KPIs. Real-time systems aren’t about prettier graphs. They’re about better outcomes. They enable automation that actually adapts, machine learning that responds to the moment, and operations that can absorb shocks without falling apart.
Trust Is Built in the Present
Real-time stream processing is no longer an edge case. It’s becoming the backbone of digital trust.
And that’s the point. You don’t build trust with apologies for late updates or refunds for processing delays. You build it by meeting people where they are—in the moment. Batch jobs won’t get you there. But a real-time streaming mindset will.
So go ahead—keep your batch jobs. Just leave them for the laundry.