Operations | Monitoring | ITSM | DevOps | Cloud

Load balancer is a system that distributes network traffic across a group of servers. AWS’s load balancing service is called ELB (Elastic Load Balancing). It automatically distributes incoming traffic across multiple targets like EC2 instances, containers, and IP addresses. It essentially acts as a traffic cop for your application, ensuring high availability and scalability.

You’re at the checkout in a supermarket, and suddenly, the payment system stops working. Frustrating, right? Unfortunately, this is an all-too-common scenario that explains why some people think twice before shopping in person. It isn't just a problem for shoppers—it's a major headache for businesses that operate across multiple locations.

Digital transformation is happening at a rapid pace. Customers demand always-on, always-mobile, instantly available experiences, while businesses look to modernize for increased productivity, responsiveness, and profitability. Amid this constant change and increasing pressure to achieve more with limited resources, IT teams struggle to keep pace, weighed down by legacy tools and operational processes that no longer scale.

Troubleshooting within Kubernetes environments can be a daunting task. If we could only have a magical artificial intelligence advisor that could gather all the data about what goes on the system, and tell me what’s wrong, and even how to solve it. Wouldn’t it be nice? K8sGPT is a young open source project that uses generative AI to give Kubernetes superpowers to everyone. It recently turned a year old, and is now part of the Cloud Native Computing Foundation (CNCF).

In the previous articles, we used freestanding applications and relied on a global Zephyr installation. In this article, we’ll see how we can use West to resolve global dependencies by using workspace applications. We first explore West without even including Zephyr and then recreate the modified Blinky application from the previous article in a West workspace.

I’m Romaric, the CEO and co-founder of Qovery. With 15 years of experience managing large-scale infrastructure and a passion for computer science, I’ve embarked on a journey to build an ambitious tech product designed to revolutionize the developer experience. At Qovery, we aim to simplify the path to production for developers, ensuring they can focus on writing code without worrying about the underlying infrastructure.

Logs are useful for troubleshooting and identifying issues in applications, as they provide a record of events and activities. However, managing log data can be challenging due to the large volume of log events generated by modern applications, as well as the need to balance the level of detail in the logs and the impact on the application's performance.

Elasticsearch and Splunk can both be used as log analysis tools for software applications. Elasticsearch, as part of the Elastic Stack, offers a highly scalable, open-source solution for real-time search and analytics across diverse data types, excelling in customization but with a steeper learning curve.

Log analysis is a very powerful feature for an application when it comes to debugging and finding out which flow is working properly in the application and which is not. In a world of containerization and cloud computing, it is essential to understand logs generated by a Docker environment to maintain healthy performing applications. In this article, we will discuss log analysis in Docker and how logging in Docker containers is different than in other applications.

OpenTelemetry is a Cloud Native Computing Foundation(CNCF) incubating project aimed at standardizing the way we instrument applications for generating telemetry data(logs, metrics, and traces). However, OpenTelemetry does not provide storage and visualization for the collected telemetry data. And that’s where an OpenTelemetry backend is needed. Cloud computing and containerization made deploying and scaling applications easier.