Operations | Monitoring | ITSM | DevOps | Cloud

On LinkedIn, there are more than thirty thousand U.S.-based DevOps positions. That number may not be as high as it is for software developers, but it’s still higher than sysadmins or systems engineers. Maybe this demand has you interested in switching career paths, or maybe you want to change jobs. If that’s the case, you’re in luck. Today’s post will present some common interview questions that you’ll probably get when interviewing for a DevOps position.

Deploying an application on Kubernetes can require a number of related deployment artifacts or spec files: Deployment, Service, PVCs, ConfigMaps, Service Account — to name just a few. Managing all of these resources and relating them to deployed apps can be challenging, especially when it comes to tracking changes and updates to the deployed application (actual state) and its original source (authorized or desired state).

Redis (which stands for REmote DIctionary Server) is an open source, in-memory datastore, often used as a database, cache or message broker. It can store and manipulate high-level data types like lists, maps, sets, and sorted sets. Because Redis accepts keys in a wide range of formats, operations can be executed on the server, which reduces the client’s workload. It holds its database entirely in memory, only using the disk for persistence.

Five worthy reads is a regular column on five noteworthy items we’ve discovered while researching trending and timeless topics. This week, we explore serverless computing and how it can impact business operations.

As we explained in Part 1, it’s important to monitor task status and resource use at the level of ECS constructs like clusters and services, while also paying attention to what’s taking place within each host or container. In this post, we’ll show you how Datadog can help you: Automatically collect metrics from every layer of your ECS deployment, Track data from your ECS cluster, plus its hosts and running services in dashboards, and more.

In Part 1, we introduced a number of key metrics that you can use for ECS monitoring. Monitoring ECS involves paying attention to two levels of abstraction: the status of your services, tasks, and containers, as well as the resource use from the underlying compute and storage infrastructure, monitored per EC2 host or Docker container. In this post, we’ll survey some techniques you can use to monitor both levels of your ECS deployment.

Amazon Elastic Container Service (ECS) is an orchestration service for Docker containers running within the Amazon Web Services (AWS) cloud. You can declare the components of a container-based infrastructure, and ECS will deploy, maintain, and remove those components automatically. The resulting ECS cluster lends itself to a microservice architecture where containers are scaled and scheduled based on need.

Lambda@Edge is a compute service that allows you to write JavaScript code that executes in any of the 150+ AWS edge locations making up the Amazon CloudFront content delivery network (CDN) service. In this post, I’ll provide some background on CDN technologies. I will also build out an application stack that serves country-specific content depending on where the user request originates from.