Skip to main content

DevOps is a Metaphor

It finally dawned on me why people struggle with defining DevOps.  If someone is truly trying to define DevOps, they will start to realize it is different in different context.  I have tried many times and each time I slant my response to the situation or what is important to me at the time.  You have seen DevSecOps, DevTestOps, DevOpsSomething and so on.  This is because people are trying to use the fundamental point of DevOps to describe more examples of it.  This made me realize that if you view DevOps as a culture, you will start using it to define more than just "dev" and "ops" because it is a metaphor.

DevOps is really a metaphor for two opposing entities that clash when required to work together because they have different goals and motivators.  Dev wants to change and Ops wants stability.  Apply the same concept to other challenges like Dev/Test, ITIL/Agile, Offense/Defense, Politics/Religion, Blacks/Whites, Husband/Wife, InSource/OutSource and many more.  Think about the core of DevOps: Transparency, Empathy, Lean Thinking, Blameless, Shared Goals, and define interactions through code. If you apply all of these concepts to each of these items, you can see how they can improve and be successful together.  You can apply this to every interaction.  The successful IT professionals are able to apply DevOps in everything they do because they get it, DevOps is a metaphor for a better way of treating humans.

PS.  You might be thinking, how can you define interactions through code between a husband and wife.  Well realistically you can't but think how much better it would be if you had every conversation, agreement or decision in a code repository to reference every time there was a dispute in a marriage.  Yes, momma would still be right but you could fork the repo when she changes her mind. :)

Comments

Popular posts from this blog

Cloud Ops: The New IT for the Cloud Era

Over the past few months of interviewing and researching dozens of companies—particularly small to mid-sized SaaS businesses—one pattern keeps emerging: the desire to stand up a Cloud Operations (Cloud Ops) organization. It makes sense on the surface. Cloud is now the infrastructure of choice, so naturally, someone needs to “own” it. But what’s unfolding in practice often misses the mark. Many companies are attempting to solve growing cloud complexity by taking all their DevOps, SRE, and platform engineering talent and consolidating them into a Cloud Ops team. The idea? Share them across product teams so no one gets overwhelmed. If that sounds familiar, it should. It’s the same centralization tactic used by traditional IT for decades. And it's creating the same problems. When Cloud Ops Becomes Old IT in Disguise Here’s the playbook we’re seeing: Move DevOps, SRE, and Ops into a central Cloud Ops team. Let them handle infrastructure, CI/CD, monitoring, and cloud securit...

2020 State of DevSecOps by Accurics

 This is an excellent report for all IT Pros and Engineers.   Highlights: Storage is most impacted solution Open security groups or network configuration Secrets are not so secret Unused resources are not secure. Take a look at these.  Look again.  These are not highly skilled problems.  They just need guidelines and proactive management.  The article uses policy as code as a solution for many of the problems.  I will drill into each of these more in the future.  I wanted to get the awareness out first and then, come back to solutions.  

How AI is Transforming DevSecOps: A New Era of Secure, Agile Software Delivery

 As software delivery accelerates and attack surfaces grow, traditional DevSecOps practices are being pushed to their limits. The integration of artificial intelligence (AI) into DevSecOps workflows is not just a trend—it’s a strategic imperative. AI is driving a seismic shift in how we manage code quality, automate security, respond to threats, and enable secure innovation at scale. In this post, we’ll explore the key ways AI is improving DevSecOps and why forward-thinking organizations are embedding it deeply into their pipelines. 1. Proactive Threat Detection and Response In modern CI/CD pipelines, code moves fast—sometimes too fast for human eyes to catch every vulnerability or misconfiguration. AI helps shift security left and right by: Analyzing code and dependencies with natural language processing and ML to detect hidden vulnerabilities, insecure APIs, or anomalous changes during commits. Real-time anomaly detection in production environments using AI-powered o...