Kernighan & Pike, The Practice of Programming, 1999 Despite constantly changing technologies and the needs of customers, some wisdom seems eternal. Programmers need to test their code. But thorough testing takes time. When we do it well, everything works, and a massive testing effort feels like a waste. However, when we do it badly, our code is often broken, and we wish that we had done better testing. I have some good news for you.

Using BigQuery object tables, we analyze unstructured Cloud Storage data with generative AI models and join the results with BigQuery structured data.

Network Telemetry, in simple terms, is like having a conversation with machines or systems located far away. It’s about collecting data from these distant sources to better understand how they’re performing. Think of it as a health check-up, but for machines or systems instead of people. This technology plays a crucial role in a variety of fields.

Welcome to the latest entry in Yellowfin Japan’s ‘How to?’ blog series! In our previous blog, we created a pie chart that aggregates on the basis of category. By using set analysis, we were able to represent the percentage of Books Only in the numeric display, based on the composition of the items ordered.

This blog post is designed as a guide to illuminate the critical importance of software testing plans—a cornerstone in ensuring the delivery of reliable and high-quality software products as we described in a previous post when we described the process of creating a complete Test Plan. For QA professionals and testers, as well as for those developers that are getting initiated on a journey into the world of testing, this post serves as a reaffirmation of our principles.

Django is a “batteries-included” framework. It has a wide array of built-in features that handle common web development tasks: URL routing, template engine, object-relational mapping (ORM), and database schema migrations. This makes it highly efficient for rapid development, allowing developers to focus on building their applications' unique aspects without reinventing basic functionalities.

As software systems become increasingly complex, observability — the ability to understand a system's internal state based on its external outputs — has become a critical practice for developers and operations teams. Traditional observability approaches struggle to keep up with the scale and complexity of modern applications. As the amount of telemetry data grows, it becomes expensive and complex to navigate. Enter AI and its promise to revolutionize observability.

Now that we have set up our API, integrated it with Kong, and connected Stripe with Moesif, we have the infrastructure to begin billing for API usage. Now, we will move into configuring Moesif with the pricing, metering, and access control/governance pieces of the API monetization journey. Let’s kick things off by setting up the prices we want to charge for API usage in Moesif.