The latest News and Information on DevOps, CI/CD, Automation and related technologies.
The reliance on digital transformation and data is ever increasing for businesses to be successful in the current environment. The agility at which the business can respond to real-life situations is proportional to the level of digitization that has been implemented in the business. For a business to nimble and agile, it is imperative that all the processes be delivered as a digital service that can be provisioned, monitored and remediated as by an automation logic at the core of the business.
When it comes to cloud strategy, companies rank “cutting costs” as their top priority for 2019, according to a recent Datamation survey. That’s not to say that they plan to cut back on cloud spending in general; in fact, those budgets are very much expected to grow. Rather, companies are looking for ways to reduce unnecessary costs and optimize cloud spend.
Kubernetes (K8s) is a prevalent open-source system for automating the deployment, scaling, and management of containerized applications. However, maintaining the service can be difficult and expensive. For that reason, it is easy to find platforms offering Kubernetes as a managed service. In this article, we will analyze three of the most popular services currently available: Google Kubernetes Engine, Azure Kubernetes Service, and Amazon Elastic Container Service for Kubernetes.
Many people writing about AWS Lambda view Node as the code-default. I’ve been guilty of this in my own articles, but it’s important to remember that Python is a ‘first-class citizen’ within AWS and is a great option for writing readable Lambda code. Take a look at these two starter examples of writing functionality in Python.
This post outlines how to build a production-grade ingress solution using Citrix ADC on Rancher. Customers can confidently expose end user traffic to microservices or legacy workloads on Kubernetes clusters on Rancher using this solution.
We’re excited to announce a new feature that enables you to install updates to your Datadog Agent integrations as soon as they are released. That means that you can make use of new or updated integrations right away, without waiting for a full release of the Datadog Agent.
This is the first of a two-part blog series. In this post we’ll use Stackery to configure and deploy a serverless data processing architecture that utilizes AWS Step Functions to coordinate multiple steps within a workflow. In the next post we’ll expand this architecture with additional workflow logic to highlight techniques for increasing resiliency and reliability.
Dear StackStorm. You have grown up. The time has come for us to part ways. You will continue the life of mature, established open-source project, with growing community. I will step aside, watch with pride your successes, and be always here to help when you need me.
