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.

If you’re like most organizations, you’re leveraging Jenkins for all sorts of things. Deployment pipelines, automated API tests, even glorified CRON jobs just to name a few.

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.

Together with the community, JFrog pioneered what we now know as DevOps with a focus on binaries (aka software packages, artifacts or images). A decade ago, no one thought binary management would be a thing — now it’s a standard most companies can’t live without. Back then, we said software universality would be necessary, and now others follow suit. People thought cloud would be a single-vendor decision.

Continuous integration (CI) and continuous delivery or deployment (CD) cover the process of automatically merging, building, and testing code changes ready for release, and – in the case of continuous deployment – releasing those changes to users. If you’re developing software for others to use, you’ll need to go through some form of build and test process before you make your latest changes available.

Over the last several years, systems architects have had to make sure their systems are cloud native, with applications that are optimized for scalable cloud technology infrastructure. In today’s environment, you should be asking whether your solutions are cloud nimble as well. For the modern enterprise, cloud computing is now the default model for applications, storage, and compute.

Many of Spot by NetApp customers run Jenkins as a core part of their CI/CD processes and use it together with Spot’s Elastigroup to deploy jobs running on spot instances. This integration has helped our customers realize up to 90% savings on cloud computing, and seamlessly fits into their existing DevOps workflows.

There are many benefits to working with JFrog Artifactory as your private Docker registry, allowing you to store, share and deploy your binary artifacts in a single source of truth. This blog post will focus on using Artifactory in Kubernetes. Specifically, we’ll walk through the steps for configuring Kubernetes to pull images from Artifactory and most importantly – scale up! It will also describe how you can enable cluster-wide authenticated access to Artifactory behind the scenes.