Data analytics is nothing new. For decades, businesses have been deploying a "stack" of data analytics tools to collect, transform, evaluate and report on data. However, as data has grown larger in volume, and as the ability to analyze data quickly and accurately has become ever-more important to business success, the data analytics stacks that businesses depend on have evolved significantly. So, if you haven't taken a look at data analytics stacks recently, they're worth revisiting.

One of the latest changes introduced by Apple is that developers needs to describe the data your app or third-party SDK collects and provide the required reasons of the APIs it uses. To do so, applications and third-party SDKs that are distributed as XCFrameworks, Swift packages, or Xcode projects, need to contain a privacy manifest file, named PrivacyInfo.xcprivacy. Table of Contents.

Before immersing yourself in this tutorial, it’s best to ensure you’ve got the following prerequisites.

We are excited to announce the launch of our new website, devresources.info, a comprehensive platform designed to serve as a central hub for developers seeking resources and events related to software development.

Calculating log analytics ROI is often complicated. For many teams, this technology can be a cost center. Depending on your platform, the cost of a log management solution can quickly add up. For example, many organizations use solutions like the ELK stack because the initial startup costs are low. Yet, over time, costs can creep up for many reasons, including the volume of data collected and ingested per day, required retention periods, and the associated personnel needed to manage the deployment.

There are several protocols featured in the Swift Standard Library that enable generic programming in Swift. By defining common behaviors and functionalities for types and collections, these protocols provide a standard way for types to interact with each other. Today we’re going to take a look at four of the key Swift Standard Library Protocols, these are: We’ll provide a brief overview of how each works, and then how we can implement them in our projects.

Are you still relying on basic stdout and stderr for logging in your Go applications? It's time to level up your logging game! Logging with stdout and stderr can be complex and limiting. Messages lack structure, context, and extensibility, making monitoring and analyzing errors hard. Manual inspection of log file is time-consuming and error-prone, especially as your application grows. But There's a better way. Join us as we explore logging libraries in Go.

Let's say you own an application program. You wanted to expand its capability and integrated several microservices to do so. Everything works fine, and you will see significant growth in customer traffic. Now, the problem slowly starts building up. You face some customer queries asking for certain fixes. You want to help as much as you can, but unfortunately, you don't know exactly where the problem has occurred. You will have to check the entire thing from the beginning. It feels like doom, isn't it?

Ever found yourself frustrated by glitches while shopping online? Picture this, you are about to make that crucial purchase, but suddenly, an error message pops up, stopping your transaction. What's behind these digital glitches, and how can they be fixed? That is where Golang logging steps in! When you encounter an error during checkout, Golang logging jumps into action, capturing crucial details about what went wrong.

If you are new to Microfrontends architecture, it’s a modern approach for splitting the frontend of your application into multiple components, so it’s easier to code and manage. We’ve actually got a separate article, introducing the concept, which you can read here. Or, if you’ve already read that introduction and now want to go a step further, just read on.