Advances in medical robotics and healthcare technology — enabled by the Internet of Medical Things (IoMT), Machine Learning (ML), and Artificial Intelligence (AI) — mean that we are already seeing less invasive procedures, more accurate diagnoses, and more personalized treatment options for patients. But all these innovations depend on software which brings increased safety and security risks.

Companies today deploy new releases constantly, some even daily. This constant deployment means regular updates are occurring or existing lines of code are being altered, which can cause problems. The slightest change of code can break the functionality of an existing code structure causing the user experience to suffer. Maintaining a visually perfect UI is important for retaining customers.

Business intelligence (BI) platforms analyze and present all your data in a visually friendly way on reports, graphs, charts, and dashboards, making it easier to view insights about your operations. But how do you get data into these platforms in the first place? The easiest way is by using BI ETL tools that extract data from its source, transform that data into the correct format, and load it into a central repository like a data warehouse — a process known as Extract, Transform, and Load (ETL).

Cloud logging services have long been plagued by limitations and high costs, hindering companies’ ability to achieve true flexibility in their operations. One of the primary obstacles is the lack of flexibility in traditional cloud logging services, which often require companies to make upfront decisions about log levels and storage capacity, locking them into fixed plans for extended periods.

Data practitioners should bring best practices from BI, especially automated data integration, to data science.

The shift from single public cloud architectures to hybrid multi-cloud continues at enterprises around the globe, as organizations look for greater control, lower costs, and improved performance. I had the opportunity recently to speak with Vaughn Eisler, director of business development at Equinix, about this massive shift and some of the complex reasons behind them. Eisler sees several reasons for the movement.

Smartphones have advanced greatly since their introduction in the 2000s. From faster GPUs for gaming to advanced instrumentation, these devices are now more computer than phone. Given the limitations of simulators and the complexity of real devices, they've also become a challenge for app developers to test. Biometric authentication, a fast-growing smartphone technology, introduced testing challenges.

Can you test 100% of your application? Or can you cover every possible testing scenario? And how much time and resources should you allocate to make the software bug-free? There are multiple questions, and all their answers lie in Test coverage techniques. In this blog, we will discuss the basics of test coverage.

Logs are essential to any application's development. Most Ruby logs are verbose and chunky, so digging for exactly what you need can be difficult. Even though they contain useful information, you might not get as much value as you should from logs if you don't know how to use them effectively. In this article, we'll explore: Let's get started.

Node.js 12.x onwards introduced async stack traces. Async stack traces allow developers to view the call stack of asynchronous code, making it easier to trace and debug code issues. In this post, we’ll see how async stack traces work, how you can use them to debug code, and how to track them using AppSignal. Ready? Let's get going!