Systems | Development | Analytics | API | Testing

"We were wasting too much time debating the accuracy of numbers as opposed to using that time to make decisions." That's how Satty Saha, Group CEO of CreditInfo-a credit bureau operating across 30+ countries-described the moment he realized his organization had a data problem. Not the kind of data problem you'd expect from a company whose business is data and analytics. An internal data problem.

Executive Summary: As enterprises integrate AI and large language models (LLMs) into their data workflows, the need for predictable, secure, and auditable database interactions has never been greater. Deterministic queries—particularly those encapsulated in stored procedures—provide the guardrails necessary for both human analysts and AI systems to access sensitive data safely.

Our customers’ worlds are complex, and for good reason. It’s multi-cloud. It’s SaaS plus on-prem. It’s Snowflake, Databricks, AWS, Azure, Salesforce, and more. Underneath every one of those choices is the same constraint: data must be accessible, stay current, and stay controlled. The hard part is getting trusted data where it needs to be, when it needs to be there, with the controls to use it responsibly.

AI is not a pilot anymore. In 2026, it is the operating agenda. And if you’re leading a business or an IT project right now, you’re probably getting the same two questions. First: “When do we see real outcomes?” Second: “Can we trust what we’re getting?” Those are fair questions. They’re the right questions. Because the truth is, the model is rarely the problem. The hard part is everything around it. The data. The access. The silos. The controls.

Data streaming with events supports many different applications and use cases. Event-driven microservices use data streaming, allowing companies to build applications based on domain-driven designs. This approach allows teams to break applications into composable microservices that can be worked on independently, speeding development. These designs scale well and can process huge amounts of data efficiently.

From shopping and food delivery to banking and fitness, mobile users everywhere expect smooth, fast, and bug-free experiences. Behind every efficient mobile app is a team of testers working hard to make that happen – and if you’re one of them, you know it’s no easy task. Mobile testing isn’t just about checking whether a few buttons work.

Complex user tasks often need multiple AI agents working together, not just a single assistant. That’s what agent collaboration enables. Each agent has its own specialism - planning, fetching, checking, summarising - and they work in tandem to get the job done. The experience feels intelligent and joined-up, not monolithic or linear. But making that work means more than prompt chaining or orchestration logic.

Have you ever noticed how your favorite apps just know what you want? Whether it’s a curated playlist that suits your mood, a movie recommendation that hits the spot, or ads that seem oddly relevant, none of it feels surprising anymore. These experiences have become so routine that we barely pause to think, “How does this even work?” But maybe we should.

Key Takeaways As companies continue to scale analytics beyond operational systems, transferring data from generic databases (like MongoDB) to cloud warehouses (like Snowflake) becomes critical.

Learn what a data lakehouse is, its core architecture, and how it compares to data lakes and data warehouses to solve modern data challenges.