In the context of a digital economy, data waits for no one. Any signal that requires a reaction, be it a stock price, customer touchpoint, or server error, needs the organization to react right not tomorrow, not later today, but now. According to veteran enterprise engineer & architect Mahitha Adapa, real-time data is no longer a nice to have, but rather an invisible force differentiating winners from laggards.

According to Mahitha, "Real-time systems were treated as a technical showcase 10 years back. Now, they are your lifeline in a changing landscape. Your systems need to be able to change quickly because if they can’t, then neither can your business.

Mahitha, who is Principal Architect for big tech, has more than a decade of experience architecting high-performance platforms at Fortune 500 companies and has witnessed the evolution of real-time data from the earliest use cases for logging and alerts to today’s streaming-first, AI integrated systems that power mission-critical decisions.

What Changed: From Buzzword to Baseline: Real-Time

In traditional enterprises, the decision-making was largely based on retrospective reports, served weekly or monthly. However, by the time the data was cleansed, aggregated, and analyzed it was usually old news. Mahitha, on the other hand, accepts that, “Batch processing is still important somewhere.” All batch processing is not useful because you might be personalizing a customer experience, fraud detection or dynamically reallocating your resources.

Three macro forces are driving the shift:

• Customer Expectations — There is a growing trend of users expecting tailored and real-time human to human interactions.
• Dynamic business conditions – The market, regulatory, and operational environment undergoes rapid changes.
• AI Maturity – As intelligent systems take charge of decision-making, access to real-time data is key to generating precise predictions.

Mahitha has been the architect for projects of her own where latency was measured in milliseconds (not seconds). Here is one such initiative she speaks of during the talk:

We built a new Streaming pipeline using Kafka and Micronaut microservices. Part of the challenge was to compress the time from event ingestion to insight to under 500ms which required us to rethink the database layer, the messaging queue, even how our services were packaged and deployed.”

The Anatomy of a Real-Time Data Stack: Why it is Expensive

So, what does it mean to actually go real-time? Mahitha explains at a high level five key architectural components:

Streaming Backbone

Often the first thing that comes to mind is Apache Kafka, but it's not about the tool at all, it's about continuous flow," she says. Ingestion and distribution of events should happen without any bottlenecks.

Responsive APIs

Mahitha has built services which are ultra-light weight with low cold starts and reduced memory footprints. “It just a matter of being responsive. In a real-time system, your API can not be the bottleneck”.

Distributed, Tuned Databases

Her systems were on Cassandra, and they have since moved on to sharding + caching on MongoDB Atlas that keeps it low-latency. Every millisecond matters when it comes to users– so, index tuning, horizontal scaling, in memory caching, and all of these actually matter more.

Event-Driven Microservices

Real-time data isn’t linear. You require services that are able to react to triggers instead of polling for service. Innovative patterns helped us scale smartly in there. That about wraps it all up.

Monitoring & Observability

In real-time, Mahitha chuckles, "Real-time systems fail in real-time. "So you require total visibility logs, tracers, metrics, preferably with anomaly detection baked in."

The AI Multiplier: Where Intelligent Automation Meets Real-Time Source

Real-time data is powerful. To this, the addition of AI compounds the transformative power, when combined with real time data.

This leads to the second part of the interview, where Mahitha said once they had streaming pipelines they needed to build those streams out further and start layering in AI. Processing data after the fact is already outdated, according to Mahitha.

Problems Teams Underestimate The Most

Mahitha is there to remind us, that as nice the hype, changing to real-time systems is not a software update, but a shift in mentality.

Here are a few of the underestimated challenges she lists:

Data Modeling in Motion:

For most teams, schemas are designed around static tables. Event-first thinking is required when building streaming systems. Is not only what data you store, it is what data you emit, when emit and how emit it."

Dealing with Errors:

“Failure is part of the flow. If your system can not handle the load, everything upstream and downstream of that system also fails. From day 1 you have to design with fallbacks and retries.

Business Buy-In:

“Real-time is expensive. Demanding cloud cost optimization, devOps maturity, and cross-team collaboration. Leadership needs convincing the investment promotes long term agility.

Why This Is Not Only a Tech Trend — It Is a Strategic Need

Is this just another hype cycle, though? Mahitha doesn’t think so.

We have moved beyond real-time being a differentiator. This gets you nowhere in many industries at this point in time, she says. Your reaction is delayed if you are not taking action based on the data as it occurs.

A 2024 report by McKinsey shows that companies that leverage real-time data analytics experience a 30% annual revenue growth rate than those that use traditional analytics. While numerous executives will tell you they put a focus on near real-time intelligence, far fewer have truly put it into practice across departments.

“Successful companies know that it is one thing to deploy a dashboard but another thing to invest in the plumbing”, Mahitha adds. It is the real-time architecture that drives real-time impact.

For the Future Real-Time Engineers and Architects

So for all those who want to grow their careers into this domain Mahitha has a simple suggestion —

• The first thing is -- you talk about not just, you know, endpoints, but you talk about understanding events.
• ‘Kafka, Flink, Airflow and them, But understand why they are there.’
• “Prepare to debug distributed systems. This is where the magic (and the pain) happens.”
• And also, don’t start go chasing ai models till your data flow is in check.” At its core, this tactic is only effective if your underlying foundation is healthy and speedy

Closing Thoughts

The next five years will only widen real-time systems, both in terms of what is needed and how much they are needed, and there will be new demands on the nature of AI agents; on the nature nature of smart distributed systems and on the nature of customer expectations, Mahitha believes.

Those companies that are going to run tomorrow will never stop, never rest, and never miss a signal around the clock, building their platforms today, she says. “Because in a 24/7 world, there is no timeframe for today — it is the only option if you want to be relevant.”