In this article we’ll learn about Sheets in SwiftUI. We will explain when, and how, to use them, we’ll explain how they’re created and shown. We’ll also look at a few examples of their usage. Let’s get started!

There is a sense of growing maturity in the DevOps market that is also reflected by its evolving tools. For the past some time, I’ve also seen these tools expanding beyond the limited utilities of continuous integration and continuous delivery. They are now seeping into database management, QA automation, and even AI integration in the SDLC. However, I see no deviation in their efficiency that enables teams to transition smoothly from ITOps to DevOps.

The end goal of the Testing Center of Excellence (TCOE) is to drive the shift from fragmented, project-based QA efforts to a unified governance structure. It was a pre-Agile innovation to bring testing from the “background” to the center of the SDLC. The arrival of Agile renders the traditional TCoEs obsolete.

Did you know that the AI market size is likely to reach USD 826.73 billion dollars by 2030? Yes, you read it right! The use of artificial intelligence can be seen in every field. AI in UX design is bringing about a revolution in the way users interact with technology. Traditionally, UX design relied on designer intuition, manual processes, and labor-intensive user testing. No doubt, the conventional approach had a positive impact on the user experience.

Let’s Take an e-commerce platform, where there are different services for user authentication, product catalog, and order processing. These services communicate through APIs. For example, the order processing service needs to get product details from the catalogue service. Contract testing ensures that the agreement between these services—specifying what data the order service expects from the product catalogue service—stays consistent.

APIs are essential to modern applications, but managing access and security policies can be complex. Traditional access control mechanisms can fall short when flexible, scalable, and fine-grained control over who can access specific resources is needed. This is where OPA (Open Policy Agent) steps in. OPA provides a unified framework for consistently defining and enforcing policies across microservices, APIs, Kubernetes clusters, and beyond. Consistent policy management is essential for enterprises.

Imagine your app faces a sudden surge in users. Thousands log in at once. Can your system handle the pressure, or will it crash?

Testing in production involves evaluating software directly in a live environment as real users. It’s becoming increasingly common.

In the world of data management, clearinghouses often serve as intermediaries, enabling secure data transactions across different platforms and ensuring compliance with industry regulations. However, as data management needs evolve, businesses are increasingly looking for alternatives to clearinghouses that offer more flexibility, control, and advanced features.

Fivetran Managed Data Lake Service brings automation, governance and reliability to data lakes.