Systems | Development | Analytics | API | Testing

I had the opportunity to attend the AWS Summit New York 2025 at the iconic Jacob Javits Center in July. The event brought together thousands of cloud enthusiasts, developers, and business leaders to explore the latest in generative AI, cloud innovation, and real-world applications across industries. From major announcements and product launches to immersive sessions and after-hours networking, the Summit delivered both inspiration and insight.

Post-migration testing is not optional - it’s essential to ensure your systems work properly after moving to the cloud. Skipping this step can lead to data corruption, performance issues, and security vulnerabilities, which can disrupt operations and lead to costly fixes. Here's what you need to focus on.

Large Language Models (LLMs) like ChatGPT and Claude offer powerful ways to extract insights from enterprise data. But connecting them directly to your backend databases—without security safeguards—can lead to disaster. A naïve setup, such as giving an LLM raw SQL login credentials, exposes your business to massive risk: credential leaks, SQL injection attacks, and unauthorized data access.

OAuth 2.0 is the leading open authorization framework that enables secure delegated access to protected resources. From traditional web apps and browser-based apps to native apps and desktop applications, OAuth allows client apps to grant access on a user’s behalf without exposing login credentials, enabling powerful third-party applications, custom data flows, and powerful user experiences. However, while OAuth is secure, it’s not always fast.

Discover insights from a recent TDWI expert panel on integrating data and AI platforms.

In software product development, many teams tend to ignore quality metrics and focus more on quantity. Such teams face challenges when building for production. They end up pushing to production very low-quality software that is filled with bugs. These bugs alone irritate and drive away product users. In 2022, research done by the Consortium for Information and Software Quality (CISQ) revealed that the cost of poor software quality in the US has grown to at least $2.41 trillion.

You're in the Sprint Review, and the team is feeling pretty good about the new feature, it’s done, the CI (Continuous Integration) pipeline is green, and they have a Friday release planned. Things are going according to plan. Then something worse happens. A test fails. But no one has an explanation. It passed yesterday. It works on my machine. Perhaps it is just the test environment again? You rerun it; green. Rerun it; red. The inconsistency starts introducing doubt. Is it an actual problem?

As Artificial Intelligence (AI) becomes deeply embedded in decision-making across fraud detection, chatbots, and virtual assistants, trust in AI agents is now critical. Users and stakeholders need clear assurance that these systems will behave fairly, clearly, evidently, and reliably in all situations. However, building that trust does not happen by chance; it requires smarter testing strategies specifically designed for the non-deterministic and robust nature of AI.

Your customers want dashboards and real-time analytics from your data. You’ve built a REST API thinking you’re providing great data access. But there’s a fundamental architectural mismatch creating hidden friction.

When dealing with sharded APIs, scaling is easier, but maintaining data consistency becomes a challenge, especially in high-latency environments. Here's the core problem: as data gets spread across multiple shards (or databases), operations like updates, reads, and transactions can lag or fail, leading to stale data, conflicts, or inconsistent states. This is especially problematic for critical applications like financial systems or e-commerce platforms.