CircleCI’s scheduled pipelines let you run pipelines at regular intervals; hourly, daily, or weekly. If you have used scheduled workflows, you will find that replacing them with scheduled pipelines gives you much more power, control, and flexibility. In this tutorial, I will guide you through how scheduled pipelines work, describe some of their cool use cases, and show you how to get started setting up scheduled pipelines for your team.
Data visualization is the process of translating large data sets and metrics into charts, graphs, and other visuals. The resulting visual representation of data makes it easier to identify and share real-time trends, outliers, and new insights about the information represented in the data. Using CircleCI webhooks, we can gather data on workflow and job events. In this tutorial, I will lead you through the steps to create a React-based dashboard to visualize this data.
Software applications consist of interconnected systems - each providing a specialized service towards the common goal of meeting a business need. As with any network, an efficient data exchange mechanism is key to its functionality, effectiveness, and responsiveness. In the past, data exchange was performed using polling requests. At regular intervals, a system would make a request to get the latest information or find out if there is an update to deal with.
Many of the multi-faceted applications development teams deploy every day are loosely coupled and every service exists to power another service. Most teams developing fullstack applications know that testing the communication between these services essential. Part of the process is testing HTTP request endpoints, and this tutorial focuses on exactly that. I will lead you through learning how to extend the k6 framework to test our HTTP endpoints.
GitOps has become a buzzword. Developers love it, because it folds DevOps into Git, a frequently used and familiar tool. Using one tool to manage multiple DevOps activities sounds fantastic, and it can be helpful for many. The truth is GitOps has limits. In this article, we explore DevOps and GitOps, compare their similarities and differences, and examine how their principles can work together to support your software development goals.
This post originally appeared on The New Stack and is re-published here with permission. In our technology-driven business climate, most companies have at least some, if not all, workloads on the cloud. And unlike on-premises networks, these cloud environments lack secure outer perimeters and specific off times. Cloud networks are always on and always available. While convenient, this also means hackers can access them at any time.
Marshmallow is a Python library that converts complex data types to and from Python data types. It is a powerful tool for both validating and converting data. In this tutorial, I will be using Marshmallow to validate a simple bookmarks API where users can save their favorite URLs along with a short description of each site.
If you are already a GitHub user, you may know that GitHub Actions provides you with powerful tools to increase efficiencies in your software delivery life cycle. Actions can be impactful for team collaborations and process simplification. For example, you can automate things like building a container, welcoming new users to your open source projects, managing branches, or triaging issues.
The Arm architecture dates back to Acorn Computers’ BBC Micro in 1981. The BBC Micro was a conventional machine, yet it ran nearly twice as fast as its contemporaries, such as the Apple II. About the same time, research on a microprocessor design based on the Reduced Instruction Set Computer (RISC) demonstrated that simple chip designs could easily outperform the current high-end 32-bit offerings.
Automating the deployment of a new web application and the release of feature updates goes a long way towards improving the productivity and efficiency of your development team. Another benefit of automation is that it minimizes or even eliminates repeated manual deployments. Manual deployments introduce the risk of human error during this critical part of the development process.
