In computer science, systems are typically divided into two categories: software and hardware. However, there is an additional layer in between, referred to as middleware, which is a software pipeline—an operation, a process, or an application between the operating system and the end user. This article aims to define middleware and reflect on its necessity, as well as address controversies about when and where it applies.

Efficiently retrieving Kubernetes metrics is the beginning of cluster performance optimization. Cluster metrics such as logs and resources descriptions are crucial as they map the health of your cluster. There’s a Kubernetes tool called K9s that lets you access your Kubernetes cluster metrics swiftly using short keys. K9s prioritizes aesthetics and performance as it displays its contents and functions in an aesthetically pleasing UI.

OpenTelemetry is a collection of tools and APIs for collecting, processing, and exporting telemetry data from software. It is used to instrument applications for performance monitoring, logging, tracking, tracing, and other observability purposes. What is Telemetry? The word is derived from the Greek “tele” meaning “remote,” and metron meaning “measure.” So, it’s the collection of metrics and their automatic to a receiver for monitoring.

Mattermost v7.2 is generally available today. The following new features are included (see changelog for more details).

The demands of a tight production schedule can make it difficult to produce consistent, readable, and concise code in the lead-up to a manual code review. This often makes the process stressful — if not outright dreaded. Standard tests are a critical part of development that verify that our code produces the expected outputs. But great tests don’t always lead to good code.

Implemented well, automation can be a powerful tool for accelerating and scaling DevOps processes to keep your team building and shipping code quickly. But knowing what and how to automate DevOps workflows can be challenging; every organization’s DevOps practices are unique, and there’s no one “right” way to approach automation. Let’s look at a few tips for approaching DevOps workflow automation to help your team move faster.

Welcome to the August 2022 edition of Open Source Matters: our regular publication about the latest happenings in open source! Let’s dive into the news.

Machine learning has crossed the chasm. In 2020, McKinsey found that out of 2,395 companies surveyed, 50% had an ongoing investment in machine learning. By 2030, machine learning is predicted to deliver around $13 trillion. Before long, a good understanding of machine learning (ML) will be a central requirement in any technical strategy. The question is — what role is artificial intelligence (AI) going to play in engineering?

Your team has worked hard on a software product for months, and it’s finally ready to release to your users! But then the worst-case scenario happens: a wide release soon indicates that the software is plagued with bugs and performance issues, resulting in poor reviews and widespread user dissatisfaction.