Jenkins is the world’s most popular CI/CD software, but — for a number of reasons — sees teams increasingly switch to cloud CI. What happens to all those Jenkins experts when teams
You’ve been handed the not-so-easy task of scoping a managed Kafka for your team. How do you start the shortlist? Post something on Reddit? Skim read a gazillion review blogs? Crash Google Chrome opening a thousand tabs to compare feature lists? If you’re going to run a Kafka POC with two or three vendors, or you’re trying to find the best Kafka for your business, how can you narrow down your selection? Let’s get to it.
By now you've heard that Atlassian is deprecating its server products. For Jira users who cannot or do not want to move to the Cloud, this is troubling news. Data Center is an option, but it's quite an expensive alternative if you do not need its minimum 500 users. Then again, it's quite disruptive to migrate to a new tool, isn't it? No one wants to interrupt their workflow by changing tools — especially one that is so central to daily operations. But it does not have to be so scary.
The purpose of a DevSecOps checklist is not to list every single action and practice you should take to ensure that DevSecOps pipeline is effective. Rather, the purpose is to help you establish the right sort of DevSecOps mindset. DevSecOps is more than a collection of best practices, it’s a shared mindset that security is the responsibility of everyone on your team.
The rapid pace of updates and upgrades to operating systems, software frameworks, libraries, programming language versions – a boon to the future of fast-paced software development, has also come to slightly bite us in the back because of having to manage these very many dependencies with their different versions across different environments.
We surveyed nearly 1,000 developers across the U.S. to uncover key development trends and insights. Today’s businesses are software businesses. If there was any positive in 2020, it’s the power software has to allow us to continue in some “normal” sense. Learn how this survey uncovers how too many companies and their development teams still have a major blind spot when it comes to errors in their code.
Python is one of the best programming resources available for designing machine learning systems. With a variety of technical abilities and potentially time-saving loops and processes, it can be an invaluable tool. However, it’s these capabilities that also make Python difficult to use. In many cases, Python may seem sluggish as it tries to navigate intricate, complicated strings of code.
On March 4, we joined 30+ organizations in their mission to improve the technology and processes related to shipping large-scale mobile apps as a part of the Mobile Native Foundation .
Over the last year, we have witnessed a shift in engineering working habits. COVID-19 forced many of us into lockdown. Instead of working from the office, coffee shops, and airport lounges, I found myself mostly working out of my (hastily built) home office. For many of us, this meant shifting back to a workstation over a trusty laptop. Not surprisingly, this did nothing to abate the heated discussion over which computers and operating systems are best for developing software.
