Operations | Monitoring | ITSM | DevOps | Cloud

Recently, we introduced support for default values in custom pipeline variables. Today, we're happy to announce the ability to make pipeline variables configuration more flexible with predefined values. We added a property to predefine values that can be assigned to a variable. It helps avoid errors, and improves the user experience. Instead of typing a variable value, you can choose it from a dropdown.

Have you ever had to purchase a CPU or a GPU? If so, you have probably come across the term “bottlenecking”. There is a certain threshold where output exceeds ability to process, and that can prevent optimal system functionality. One of the methods used in computing to overcome these bottlenecks is multi-threading, where requests are processed simultaneously by multiple threads. We can apply a similar principle to downtime monitoring.

With progressive delivery, DevOps, scrum, and agile methodologies, the software delivery process has become faster and more collaborative than ever before. Scrum has emerged as a ubiquitous framework for agile collaboration, instilling some basic meetings and roles into a team and enabling them to begin iterating on product increments quickly. However, as scrum teams grow and systems become more complex, it can be difficult to maintain productivity levels in your organization.

The software testing data provided by CI/CD tools is valuable, but it is not always comprehensive enough to give managers the insights they need to make improvements. To make effective business decisions, managers need visibility into the entire testing process, in a way that will help them understand what needs to be done and how.

Last year we had a look at managing local groups with the custom groups promise type. As you may or may not recall, we used JSON-strings to imitate CFEngine bodies. This was due to the fact that the promise module protocol did not support bodies at that time. Today, on the other hand, we’re happy to announce that as of CFEngine 3.20, this will no longer be the case. In this blog post we’ll introduce the long awaited feature; custom bodies.

Over the years, containerization has grown in popularity among organizations and will continue to grow. For example, containerization is slowly becoming the de facto approach when it comes to building applications following the microservices architecture. It is undeniable that containerization has a lot of advantages, but these advantages come at a cost.

Over the past several years, I've helped numerous customers migrate SQL Server workloads to Azure SQL, including Azure SQL Database, Azure SQL Managed Instance, and Azure SQL Virtual Machines. In this article, I'll explain some of the challenges of optimizing the compute cost for an Azure SQL Database deployment and review how the serverless compute tier can greatly simplify it.

Enterprises are dealing with a deluge of observability data for both IT and security. Worldwide, data is increasing at a 23% CAGR, per IDC. In 5 years, organizations will be dealing with nearly three times the amount of data they have today. There is a fundamental tension between enterprise budgets, growing significantly less than 23% a year, and the staggering growth of data.

Recently, a critical out-of-bounds vulnerability, assigned to CVE-2021-44142, was disclosed in Samba versions prior to 4.13.17. The Samba vulnerability carries a critical CVSS of 9.9 and allows attackers to remotely execute code on machines running a Samba server with a vulnerable configuration. The vulnerability was disclosed as part of the Pwn2Own Austin competition where researchers are challenged to exploit widely-used software and devices with unknown vulnerabilities.

Just like many other production DevOps engineering teams, our JFrog team deploys new version releases several times a day to AWS, Azure and GCP, across more than 20 cloud regions. This process used to take us many hours and could have even failed if it was done alongside maintenance by other teams.