Operations | Monitoring | ITSM | DevOps | Cloud

For those of you who aren’t familiar with AWS CodePipeline, it’s a continuous integration and continuous delivery (CI/CD) framework that enables application development teams to deliver code updates more frequently and reliably. You may have also heard it being called a CI/CD or DevOps pipeline. These pipelines have always traditionally been used to deploy the components of a certain application whenever new code in “checked-in”.

Here at Pandora FMS we love news. If it were up to us we would wear new dresses and stilettos every week, we would open headquarters in an unknown tropical country and we would change styles, to other more daring and exotic, in our cocktail parties. Just to make our love for the avant-garde clear once and for all.

A platform-agnostic way of accessing credentials in Python. Even though AWS enables fine-grained access control via IAM roles, sometimes in our scripts we need to use credentials to external resources, not related to AWS, such as API keys, database credentials, or passwords of any kind. There are a myriad of ways of handling such sensitive data. In this article, I’ll show you an incredibly simple and effective way to manage that using AWS and Python.

Kubernetes applications are increasingly making their way to the edge and embedded computing. Storage will quickly follow as the applications that rely on this edge infrastructure become more advanced and naturally carry more state. According to a study by McKinsey and Company, a “connected car” processes up to 25GB of data per hour.

Choosing a software solution means checking the right boxes, and being “cloud native” has bubbled to the top of most lists. But cloud native is a box built of boxes of its own. For a CI/CD solution like JFrog Pipelines, cloud native starts by being “born in the cloud” to natively execute in, build for, test in, and deploy to cloud computing environments. But what boxes does a CI/CD solution really need to tick to complete that larger cloud native box?

This is a follow up to a blog series where we explore DIY cloud cost management, monitoring, and optimization tools. In the first, we investigated how Lyft, Netflix, Segment, Expedia, and Slack built theirs. Check it out here. “Build versus buy” is the eternal question for every engineering and DevOps team setting out to solve a technical problem. In the cloud cost monitoring and optimization world, both options can take on many forms.

In this article we will focus on our on-premise platform, or cloud monitoring after having installed Pandora FMS console in Microsoft Azure. The installation will be made with an automated script that installs the Community version and with a second script, it allows to update Pandora FMS to its Enterprise edition (Corporative), leaving a 30-day test version (Trial).

Running your IT infrastructure from the cloud can be a smart choice for modern businesses. Cloud computing allows you to build a scalable, flexible architecture at an affordable price. But few businesses are 100% on the cloud. Businesses may have legacy systems or specific resources that work better on-premises, or they may have made a significant investment in on-premises hardware.