Systems | Development | Analytics | API | Testing

You’ve spent months building a modern and intuitive app. It's fast, user-friendly, and visually consistent. But your embedded analytics is still a clunky iframe that is totally disconnected from your UX. Users get frustrated, and engagement flatlines. In today's data-driven business landscape, embedded analytics has become a critical competitive differentiator.

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.

Intellectual property (IP) in semiconductor design refers to reusable design components that can be integrated into a larger chipan IC, SoC, or chiplet. These design blocks may be developed in-house or licensed from third-party vendors and are used in system-on-chip (SoC) design and production. With growing SoC complexity, increased market demand, and the rapid pace of innovation, adopting an IP-centric design approach is critical for staying competitive.

Multi-agent workflows are the latest technological gen AI advancements. In this blog, we explore how to develop such systems, overcome operational challenges, improve system observability, and enable seamless collaboration between agents in complex AI pipelines. We’ll cover architecture, A2A and MCP protocols and introduce Google Cloud’s agentic marketplace.

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.

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.

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.

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?

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.

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