Systems | Development | Analytics | API | Testing

In this episode, Derric Gilling, CEO of Moesif, sits down with Mike Amundsen, a leader in the API ecosystem, to explore the evolving role of observability in API design and customer experience. They break down the differences between machine vs. customer observability, best practices for tracking key metrics, and the importance of designing observability into APIs from the start.

Top tech companies thrive on scaling operations, driving rapid growth, and carving out opportunities for expansion. But what happens when your tech can’t keep up? Every app needs a way to provide developers with the visibility they need to find, fix, and prevent errors and performance issues. An end-to-end observability solution equips business leaders with the insights they need to make data-driven decisions – ultimately boosting customer conversion and satisfaction.

The concept of caching is an important one in computer science and is directly relevant to building web apps. At a high level, caching takes advantage of the fact that memory lookups are faster and less compute-intensive than database queries. If you have a database query that might get made repeatedly, caching the result in a cache speeds up that flow of execution. Rails applications are no exception as they often use memory-based caches like Redis to cache data and improve application performance.

In today's competitive B2B landscape, seamless data integration enables marketers to overcome data challenges, driving agility, efficiency, and smarter decision-making.

Dropdown menus are important for user interactions in web applications, allowing users to choose from a pre-defined dropdown list. Selenium WebDriver simplifies dropdown handling through the select class, which provides various methods to interact with dropdown elements efficiently. Read further to understand the use of select class in Selenium scripts for your automated tests to interact with a dropdown element and navigate options effectively.

Companies need to adapt quickly to stay ahead of their competitors. This is where data-driven agility becomes essential. By leveraging real-time data, businesses can immediately respond to market changes and confidently make informed decisions. This article will explain data-driven agility, how it works, and why it’s a valuable approach for any organization. At its core, data-driven agility involves using live data to predict and respond to changes.

Easy access to data is a key part of any successful business strategy. Accessing, sharing, and analyzing data quickly helps organizations make smarter decisions, streamline operations, and stay competitive. However, many businesses face a significant challenge: they collect vast amounts of data from different sources yet often lack the right tools, processes, or infrastructure to make that data easy to access and use across the company.

Relying on intuition alone isn’t enough to stay ahead of the game in today’s fast-paced business environment. Success now comes from smart, data-driven decision-making backed by real-time insights and analytics. With stream processing and modern analytics platforms, businesses can collect, process, and analyze information as it happens, giving them a clear edge.

Over the last two decades, I have used several bug and project management tools, such as ClearQuest, BugZilla, and Atlassian's Jira. Jira is a popular tool for managing and tracking projects. It is widely regarded as a flexible and efficient system that offers interesting features such as JQL (Jira Query Language) and personalization options.

25% to 35% of a software testing team’s time is spent on writing and maintaining test cases. Yet, poorly written or incomplete test cases can lead to missed defects, inefficient testing, and costly rework.