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.

Organizations are building data lakes and bringing data together from many systems in raw format into these data lakes, hoping to process and extract differentiated value out of this data. However, if you’re trying to get value out of operational data, whether on prem or in the cloud, there are inherent risks and costs associated with moving data from one environment to another.

Microfrontends is a modern front-end architectural approach, by which web applications are segmented (or decomposed) into smaller self-contained units, allowing for easier management and scalability. You can our introduction article to Microfrontends to understand better this architecture approach. Today we’re going to provide a step-by-step guide to the process of building a real-world application using React and Module Federation.

In computing, logging involves maintaining a record of events within a computer system, encompassing issues, errors, or pertinent information about ongoing operations. These events can manifest within the operating system or other software, with each occurrence documented through the creation of a message or log entry. Logging is crucial for comprehending the application's behavior, aiding in the identification of unexpected issues, debugging, or merely monitoring events.

Javascript Object Notation (JSON) is becoming the standard log format, with most modern applications and services taking advantage of its flexibility for their logging needs. However, the great flexibility for developers quickly turns into complexity for the DevOps and Data Engineers responsible for ingesting and processing the logs. That’s why we developed JSON FLEX: a scalable analytics solution for complex, nested JSON data.

In iOS, WebSockets enable real-time communication between a client (an iOS app) and a server. Unlike traditional HTTP connections which are stateless, short-lived and request data on demand, WebSockets use a single long-lived connection to send and receive data simultaneously. This significantly reduces response times which is crucial for apps that rely on instant updates and live data to support functions such as chat and multiplayer games.

On top of their industry-leading cloud infrastructure, Amazon Web Services (AWS) offers more than 15 cloud-based analytics services to satisfy a diverse range of business and IT use cases. For AWS customers, understanding the features and benefits of all 15 AWS analytics services can be a daunting task - not to mention determining which analytics service(s) to deploy for a specific use case.