AI Is About to Crush Your Database!
AI agents won’t just increase traffic—they’ll overwhelm legacy databases unless core data architectures evolve to handle machine-scale demand.
Systems | Development | Analytics | API | Testing
Modern Software Development For Today's Systems & Applications
Collections: A New Way to Tackle Content Clutter with ThoughtSpot
Eliminate the chaos of content sprawl and bring structure to your analytics environment. In this feature deep-dive, we explore Collections, a powerful new way to organize, navigate, and share your ThoughtSpot assets using a smart, hierarchical folder system. In this video, you will learn how to: The Result? A radically cleaner user experience that empowers executives to find answers faster and allows admins to govern content with surgical precision.
Capturing User Logins for Business Intelligence Insights with ThoughtSpot
Bridge the gap between deploying analytics and driving actual adoption by capturing and analyzing real-world user login behavior. In this technical walkthrough, we explore how to utilize ThoughtSpot CS Tools to extract audit logs and activity data, giving admins and stakeholders clear visibility into platform engagement. In this video, you will learn how to: The Result? A data-driven approach to platform administration that replaces intuition with hard evidence of user adoption and engagement.
Building for Agentic AI
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.
Making Data Work for AI
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.
Qlik: Making Data Work for AI
AI is moving fast, but outcomes still depend on one thing: trusted data, in the right place, at the right time, with the right controls. In this short Qlik story video, you’ll see how we help teams accelerate AI with confidence, turning data into answers you can explain, and actions you can stand behind. From strengthening supply chain decisions, to building a campaign plan in seconds, to spotting changes as they happen, Qlik connects analytics, automation, and governed AI experiences, so AI becomes operational, not experimental.
Streaming Data Integration with Apache Kafka
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.
The Top 10 Challenges with Mobile Testing (and how to solve them)
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.
Why orchestrators become a bottleneck in multi-agent AI
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.
2026 Guide To Integrating AI Into Existing Apps
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.