Why Real-Time Intelligence Across Azure, Databricks and Fabric Is a Game Changer for Enterprises

By Thakshila, Data Specialist at Novon

Adopt cloud-based data management and cloud-based data storage.

Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.

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“Real-time intelligence is where the true value of data comes to life,” says Thakshila, a data specialist at Novon. “It’s not just about collecting information. it’s about being able to act on it as it’s being generated. Whether it’s spotting fraud, adjusting inventory to match sudden demand, or triggering predictive maintenance, this is data at its most powerful.”

According to Thakshila, real-time intelligence allows businesses to handle vast volumes of time-sensitive data and make decisions when they matter most. “It centralises data in motion. That means we can query, transform and gain insights from structured and unstructured sources while they’re moving through the system not hours or days later.”

Real-Time Intelligence in Azure, Databricks and Microsoft Fabric

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“Each of the big platforms Azure, Databricks, and Microsoft Fabric approach real-time intelligence differently, and that’s what makes this space so interesting to me,” says Thakshila.

She explains: “Databricks is my go-to when performance and scale matter most. Its Structured Streaming engine is incredibly powerful, especially for machine learning and advanced ETL workloads. If you’re doing high-volume, low-latency processing and your team is comfortable with complex engineering Databricks is the way to go.”
On Azure, Thakshila shares: “For log processing, event ingestion, or IoT data, Azure’s native services like Event Hubs, Stream Analytics and Data Explorer are spot-on. They’re modular, integrate easily with existing Azure environments, and you can get dashboards up and running with minimal effort.”
Then there’s Microsoft Fabric. “Fabric is the most exciting for business users. It’s not designed for ultra-high frequency decisioning, but for real-time visibility and operational dashboards—it’s a game changer. I’ve watched teams spin up live dashboards in minutes, trigger alerts, and use Copilot to surface insights all without touching code.”

Choosing the Right Tool Starts with the Right Questions

“One of the biggest mistakes I see is businesses jumping straight into a tool because it’s popular or powerful,” Thakshila says. “What they should be doing is asking: what are we trying to solve? What’s the latency requirement? What kind of data are we working with?”

She adds, “The technology should serve the business not the other way around.”

What’s driving a modern Data Architecture?

In today's digital economy, data is a critical asset that drives business innovation, efficiency, and a competitive advantage. A modern data architecture is at the heart of leveraging this business asset. It is designed to address the complexities and scale of managing data in a rapidly evolving technological landscape. So, what are the key tenants of a modern data architecture that allow for this agility, innovation, and security of your organisations data.

Adopt cloud-based data management and cloud-based data storage.
Add a data fabric architecture for seamless integration. Organisations are looking for flexible data management solutions as data continues to sprawl across disparate destinations—on-premises data centres, multiple clouds, and edge devices.
Add a data mesh architecture to simplify and focus your response to the changing data landscape. It will enable your organisation to respond quickly and cost effectively to the data changes that abound in 2024
Adopt automation using generative AI and ML in data management.
Adopt low-code / no-code for data integration. Find the vendors or data specialists that have completed work similar to your challenge. Successful experienced operators are key.
Provide data governance, security, and privacy in an automated fashion. There is no time in 2024 to manually rediscover and develop data changes that other organisations have already found and adopted. These changes must be automated through generative AI and ML capabilities.

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Data management guiding principles still apply in today’s data landscape, however, today change is very rapid and there is no room for a bottom-up approach to managing your data, rather you should build pervasive AI / generative AI / ML models that do the heavy lifting for you.

In 2023 and still today we talk about data gravity as a key challenging principle of today’s modern enterprise data platform, but increasingly anti-data gravity is seen as the key issue in 2024 and beyond.

Data gravity is well documented, essentially this is where the accumulation of data (operations and analytical) attracts more data and services into its business data mix, thereby increasing data complexity and potentially causing serious challenges for an organisation to maximise the value of their data, especially if you have adopted a cloud first approach for your infrastructure. To further complicate this scenario, consider the challenges if you are a global organisation as you manage across different time zones, regulatory and business structures.

Anti-gravity advocates that the data and expertise should stay local and the two modern data architectures when used correctly can help moderate these challenges are data fabric and data mesh architectures.

When to Use Databricks

“Databricks isn’t the simplest tool for business users,” says Thakshila, “but for engineering-heavy environments with serious performance requirements, it’s unmatched. The structured streaming engine gives you full control and excellent scalability.”

She emphasises that Databricks is best when advanced data transformations or machine learning are in the mix. “It’s built for complexity—if that’s what you need.”

When to Use Azure

“Azure is brilliant for modular, event-driven solutions,” Thakshila explains. “It works really well when you need to process IoT data, system logs, or user activity in real time.”

She points out, “The ease of integrating with Power BI to build dashboards and monitor activity is a real win. I’ve deployed Stream Analytics jobs that connect to Event Hubs and light up a Power BI report in minutes.”

Microsoft Fabric: Real-Time for the Business User

“Fabric is for the analysts or ops teams,” says Thakshila. “If your team needs real-time insight but doesn’t have data engineering capacity, this is the most accessible tool on the market.”

She adds, “The tight integration with Power BI and OneLake, plus the Copilot features, means that users can take control of their own analytics. It’s not the tool for high frequency or sub second decisioning. But for operational business intelligence and end to end visibility, it brings real time analytics to a whole new audience. It’s not for microsecond latency, but it gets the job done for operational dashboards and alerts.”

Real Advice: Fit for Purpose, Lean and Cost-Aware

“Start with purpose, not hypotheticals,” Thakshila advises. “I see far too many systems built for imaginary use cases. Design for what your business actually needs.”

One of the first things she recommends is looking for cost sinks. “Eliminate duplication. I’ve helped companies save tens of thousands simply by reducing how often certain queries are run. High-frequency dashboards are expensive when they don’t offer high-value insight.”

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She also champions lean data management. “Massive data warehouses aren’t always necessary. Reduce complexity by breaking things down to the datasets you really need.”

And don’t forget about licensing. “Licences are often an invisible cost. I always recommend a review—especially when tools charge per user. It’s a quick win.”

The Bottom Line

“Real-time intelligence isn’t just a trend—it’s a necessity,” says Thakshila. “It’s about staying competitive, agile and proactive.”

She concludes, “The goal isn’t just to be fast—it’s to be fast where it matters. That’s what we help our clients do at Novon: build fit-for-purpose data strategies that make real-time intelligence work in the real world.”