Have you ever wanted to see the work of your entire engineering organization in a visualization as it happens? In this article, I'll tell you how I used Github webhooks and Netlify serverless functions, along with a simple Svelte web app, to do just this in my new interactive visualizer tool.
One of the main attributes of low-code development is speed. With the Appian Low-Code Platform, building, modifying and executing enterprise applications is fast and easy. And data is what powers those applications. That data has to be organized and managed, and this can slow down the development process.
The pace of the industry today is pressuring software developers to build, test, and release software more frequently than ever. To achieve this pace, teams have built two core processes into their workflow: Continuous Integration and Continuous Deployment. Continuous Integration and Continuous Deployment (CI/CD) is a subset of the DevOps workflow that automates application code management and its safe, predictable shipping. In this article, we’ll take a deep dive into CI/CD.
Complex modern systems are the new reality for infrastructure teams, and this is due to the evolution of Cloud Computing and working with Distributed systems, containerization, and microservices by default. The teams now have different infrastructures and virtual services with which they must take care of scalable, reliable, and performative applications.
In the first part of this two-part series on memory leaks, we looked at how Ruby manages memory and how Garbage Collection (GC) works. You might be able to afford powerful machines with more memory, and your app might restart often enough that your users don't notice, but memory usage matters. Allocation and Garbage Collection aren't free. If you have a leak, you spend more and more time on Garbage Collection instead of doing what you built your app to do.
In this article, we will walk you through steps to run a Jenkins server in docker and deploy the MLRun project using Jenkins pipeline. Before we dive into the actual set up, let’s have a brief background on the MLRun and Jenkins.
It says something about a company and its people when they drop the process of formulaic job interviews and just let you pitch ideas for the job you want. That’s what happened when I applied to Continual as a Technical Marketing Manager. Five weeks in, I’m pleased to say I’m working on those same ideas, which I’ll detail in a couple minutes.
Cloudera has a strong track record of providing a comprehensive solution for stream processing. Cloudera Stream Processing (CSP), powered by Apache Flink and Apache Kafka, provides a complete stream management and stateful processing solution. In CSP, Kafka serves as the storage streaming substrate, and Flink as the core in-stream processing engine that supports SQL and REST interfaces.
