The World Economic Forum projects that 463 exabytes of data will be created globally every day in the coming year. To put that into perspective, that’s over 200 million DVDs worth of data produced daily. But that shouldn’t be surprising: from personalized shopping recommendations to traffic updates to remote work applications, our lives revolve around data.
As our reliance on data grows, so do the challenges of managing its exponential growth. The popularity of IoT devices, the push for smarter cities, and the near-universal adoption of cloud computing all demand systems capable of handling unfathomably massive and continuous data flows. Abhishek Gupta, Senior Engineering Technical Leader at Cisco, is a leading authority on systems architecture and cloud technologies. Drawing on his experience in healthcare, retail, and government, Gupta offers an insider look at the evolving role of big data—and why it should matter to you.
The Foundations of Big Data: Cloud & Scalability
Big data can get dismissed as just another tech buzzword, but it serves as the backbone of systems we interact with every day. “When you check your email or stream a video, you’re relying on networks of data systems working in lockstep,” Gupta explains. “When these systems perform well, they’re invisible. But the complexity under the hood is staggering—and it’s only growing.” This increasing complexity, he argues, will shape which technologies thrive or fail in the coming decade.
The root of the problem is scalability—the ability of a system to handle increasing demands without breaking down. Gupta compares it to urban infrastructure: “A two-lane road works fine for light traffic, but when an entire city starts using it, you either add lanes or need to rethink your city layout.” Without scalability, digital “traffic jams” can lead to service disruptions, such as delayed banking transactions or missed alerts in healthcare monitoring systems. Not everyone can afford a second chance with scalability.
Cloud computing and distributed systems have become the modern equivalent of these data city planning upgrades. Platforms like Amazon Web Services and Microsoft Azure allow businesses to scale their resources on demand. “The cloud is essentially a shared workspace for data,” Gupta explains. “It allows organizations to access immense computing resources at a fraction of the cost.”
This flexibility has been a boon for legacy industries with limited IT budgets, allowing them to modernize without heavy investments in infrastructure, and their popularity has yet to wane. Gupta’s expertise includes designing cloud-native systems that optimize performance for these same industries. In his DZone article, he highlights how businesses can leverage cloud-native architectures like Kubernetes to distribute workloads and improve fault tolerance by correctly using their pods and services.
Keeping the Lights On: Network Reliability
Most people don’t think about network reliability—until something goes wrong. “Network assurance is, conceptually, a GPS and traffic controller for data,” Gupta says. “It’s intended to identify bottlenecks and reroute information before users are affected, or prevent disruptions before they escalate.”
Failures in network reliability can have costly consequences. A system failure during Black Friday sales could cost businesses millions, while delays in transmitting critical weather data during a hurricane could endanger lives. To mitigate these risks, businesses are increasingly relying on AI-driven network assurance tools, which Gupta describes as “self-healing” systems. These tools use artificial intelligence to proactively identify and resolve potential network issues, ensuring smooth operations even under peak loads.
The shift from reactive troubleshooting to preventive strategies benefits both consumers and businesses, says Gupta. It ensures that digital experiences—whether streaming a movie or completing an online transaction—remain seamless, building trust and reliability in the systems we depend on daily.
Making Data Work in Real Time: Stream Processing
One of the most widely used innovations in big data is stream processing—the ability to analyze and act on data the moment it’s created. Unlike traditional batch processing, which works in chunks, stream processing handles data continuously.
Gupta illustrates the concept with live sports streaming. “Player stats, scores, and every interaction need to be updated for millions of viewers and services simultaneously, instantly,” Gupta explains. “Stream processing makes that level of responsiveness and upkeep possible.”
Of course, the applications go beyond just entertainment. Financial institutions rely on stream processing for fraud detection, flagging suspicious transactions as they happen, while smart cities deploy it to dynamically manage traffic and power autonomous vehicles. Frameworks like Apache Kafka and Apache Flink, which Gupta frequently discusses in his HackerNoon articles, are critical tools in enabling this responsiveness.
The Future of Big Data: Opportunities & Responsibilities
While the growth of big data offers immense opportunities, it also raises pressing ethical questions, particularly around privacy and security. Gupta emphasizes that how society manages these trade-offs will define the next era of networking and connectivity.
“The promise of big data is its ability to turn overwhelming complexity—at a scale that humans simply can’t understand and see—into insights and useful information,” Gupta continues. However, he warns that this potential hinges on building robust systems that can securely process vast amounts of information without compromising individual privacy. Collaboration between industries, governments, and technologists will be essential to navigating these challenges responsibly.
Whether you’re a business leader planning your company’s digital transformation or a consumer relying on data-driven services every day, the systems that power big data impact us all. Understanding how they work—and how they’re evolving—can help you navigate a future that’s increasingly built and shaped by data.