We think rightly so. One of the foundations of DevOps is that cross-functional teams deliver better products faster. If that is the case, then why keep their collaboration platforms separated, why make their work flows independent and disconnected, why hand off deliverables when it should be all about constant iteration with handovers?
Figma founder, Dylan Field, crunched some numbers that prove this point and found out that the designers:developer ratio had increased considerably among top players
And we are not talking here about adaptable, flexible startups. Quite the contrary. To quote Dylan:
The companies willing to go on the record were mostly enterprise, so this sample doesn’t even include the consumer startups that famously focus on design, like Airbnb. Facebook staffers told us the social network has quadrupled its designer hiring target in the last two years alone — but Facebook wouldn’t officially comment.
At GitLab, in line with DevOps learnings, we believe that frictionless feedback loops are the best way to validate one’s work. Giving designers the ability to work hand in hand with the developers that will create the source code that will later give life to their visuals is making that feedback loop the fastest in our opinion.
It is not only about speed though. GitLab aspires to be a comprehensive and versatile collaboration platform at all levels. It should make our users able to perform laser-focused code reviews at the line level or comments at the pixel level. At the same time, being able to discuss in an orderly fashion the big picture would provide that scope of collaboration that software development requires: fast incremental iterations that integrate in a product or service deployed in multiple environments. The ability to improve software adoption and engagement everywhere anywhere in the Cloud by deploying well-informed, minuscular and cooperative changes at the team level.
There are many definitions of DevOps of which a simplification that most would agree with is the holistic view of software development and operations in a virtuous cycle of versioning, incremental releasing and short feedback loops. This approach, in general has proven successful to many of the software giants that deliver software used by millions with consistent levels of reliability.
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DevOps and Cloud computing are joined at the hip, now that fact is well appreciated by the organizations that engaged in SaaS cloud and developed applications in the Cloud. During the COVID crisis period, most of the organizations have started using cloud computing services and implementing a cloud-first strategy to establish their remote operations. Similarly, the extended DevOps strategy will make the development process more agile with automated test cases.
According to the survey in EMEA, IT decision-makers have observed a 129%* improvement in the overall software development process when performing DevOps on the Cloud. This success result was just 81% when practicing only DevOps and 67%* when leveraging Cloud without DevOps. Not only that, but the practice has also made the software predictability better, improve the customer experience as well as speed up software delivery 2.6* times faster.
3 Core Principle to fit DevOps Strategy
If you consider implementing DevOps in concert with the Cloud, then the
below core principle will guide you to utilize the strategy.
Guide to Remold Business with DevOps and Cloud
Companies are now re-inventing themselves to become better at sensing the next big thing their customers need and finding ways with the Cloud based DevOps to get ahead of the competition.
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Once an industry term becomes popular, particularly in technology, it can be difficult to get an accurate definition. Everyone assumes that the basics are common knowledge and moves on. However, if your company has been discussing DevOps, or if you are interested in learning more about it, here are some basics you should know.
DevOps refers to the restructuring of the traditional software application cycle to support Agile development and continuous improvement/continuous delivery. Traditionally, the software was created in large-scale, monolithic bundles. New features and new releases were created in large packages and released in full-scale, infrequent, major deployments.
This structure is no longer effective in the modern business environment. Companies are under increasing pressure to be agile. They must respond rapidly to changes in the business environment to remain competitive. Software development needs to be completely changed as a process so that incremental improvements can be made frequently – ideally, several times per day.
However, changing a development lifecycle completely requires major changes – in people and culture, process, and enabling tooling – to be effective. DevOps was created by the breaking down of cycles between development and operations, combining two separate functions in application development. These changes intend to support agile, secure, continuous improvements, and frequent releases.
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DevOps is supposed to help streamline the process of taking code changes and getting them to production for users to enjoy. But what exactly does it mean for the process to be “streamlined”? One way to answer this is to start measuring metrics.
Metrics give us a way to make sure our quality stays the same over time because we have numbers and key identifiers to compare against. Without any metrics being measured, you don’t have a way to measure improvements or regressions. You just have to react to them as they come up.
When you know the indicators that show what condition your system is in, it lets you catch issues faster than if you don’t have a steady-state to compare to. This also helps when you get ready for system upgrades. You’ll be able to give more accurate estimates of the number of resources your systems use.
After you’ve recorded some key metrics for a while, you’ll start noticing places you could improve your application or ways you can reallocate resources to where they are needed more. Knowing the normal operating state of your system’s pipeline is crucial and it takes time to set up a monitoring tool.
The main thing is that you decide to watch some metrics to get an idea of what’s going on when you start the deploy process. In the beginning, it might seem hard to figure out what the best metrics for a pipeline are.
You can conduct chaos engineering experiments to test different conditions and learn more about which metrics are the most important to your system. You can look at things like, time from build to deploy, number of bugs that get caught in different phases of the pipeline, and build size.
Thinking about what you should measure can be one of the harder parts of the effectiveness of the metrics you choose. When you’re considering metrics, look at what the most important results of your pipeline are.
Do you need your app to get through the process as quickly as possible, regardless of errors? Can you figure out why that sporadic issue keeps stopping the deploy process? What’s blocking you from getting your changes to production with confidence?
That’s how you’re going to find those key metrics quickly. Running experiments and looking at common deploy problems will show you what’s important early on. This is one of the ways you can make sure that your metrics are relevant.
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