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DevOps Journeys - Guest Blog with John Boero

Guest Blog with John Boero

“Well-designed initial builds can eliminate the need for continuous runtime monitoring."

This principle is at the heart of modern DevOps efficiency, and in our latest guest blog, John Boero explores how getting infrastructure right from the start can drastically reduce operational overhead. He delves into the evolving landscape of DevOps, from the rise of AI-driven automation to the expanding role of Platform Engineering in streamlining development workflows.

DevOps has transformed the way organisations build, deploy, and manage software. In your experience, how has the DevOps landscape evolved in recent years, and what do you see as the most significant shifts driving its growth and adoption today?

In recent years DevOps services have expanded into specialties like DevSecOps and Platform Engineering. Having additional skills like security or always was more valuable in your career, with DevSecOps roles or contracts typically paying about 50% more in a given market. In the year 2024 the industry has seen a big push for cost reduction, with rates dropping across the board. On the product side price changes have shaken up priorities a bit. For example, there was typically a dominant IaC option in Terraform. As more competitors have entered the market with aggressive pricing, options have expanded a bit and SMEs are expected to know more tooling options.

With the growing adoption of AI and machine learning in DevOps, how do you see AIOps transforming traditional practices, and how are you incorporating these technologies into your processes?

Personally I believe AIOps and MLOps are going to drastically alter the landscape within the next year. With AI experts pursuing Artificial General Intelligence (AGI) the DevOps profession is at serious risk of being replaced by bots. This is one reason I've been focusing on GenAI from a DevOps perspective for at least 2 years now. Chatting with an LLM is one thing, pasting some code or scripts into a shell and running them is another. RAG agents that can automatically run scripts have existed for almost 2 years now and I've seen some impressive demos. The startup ecosystem in the US is already brewing next year's innovation in this sense and I'm a big believer that progress will march forward. My suggestion is to embrace it and learn all you can about ML so that you are viewed as an expert babysitter and QA of future AI assistants.

How are you leveraging platform engineering to enhance developer experience and streamline operations? What challenges and benefits have you encountered in implementing this approach?

As I view Platform Engineering, it's all about maximizing build-time efficiency so runtime operations are as smooth as possible. Having the correct container registry built so that a business can use clickops to instantly deploy their application in an auto-scaled, managed method is the ultimate dream of service catalog products like ServiceNow. Re-emphasizing the background work to prepare this is a great focus of Platform Engineering and in my mind, brings back simplicity to the entire business process.

With the increasing emphasis on developer productivity and autonomous teams, how do you balance self-service models with governance and security requirements?

In the open source space there are a variety of domain specific languages (DSL) being used for collaborative DevOps. Some of these languages are procedural and some are declarative. Declarative languages like JSON offer schema support which provides built-in guardrails for mistakes or restrictions. I'm a big proponent of schemas for limiting access. Where schemas are unavailable, Open Policy Agent (OPA) is a good standard for restricting code. Workflows from the code onward typically just rely on approvals. Automating rejections is easy. Automating approvals is trickier. This is one reason layer 8 human intervention is still required.

As the need for continuous compliance grows, how are companies integrating security into their CI/CD pipelines, and what strategies are they using to ensure compliance in dynamic cloud environments?

Compliance is one criterion that is usually easy to test at build time. This saves a lot of runtime profiling. Compliance shouldn't require an agent continuously checking for drift if the initial build was designed correctly. This is another reason we've seen the rise of immutable OS platforms and Kubernetes with read-only container space. The less opportunity to alter an official build the less need for live scanning. Every build pipeline should ensure this. There has been a rise in vendors providing runtime container scanning with really clever solutions but in an ideal world this wouldn't be required.

In the era of GitOps and Infrastructure as Code (IaC), how do you ensure best practices for version control, automation, and consistency across environments?

Often a vendor can't even give a straight answer on the concept of best practices. Personally I've been in multiple environments preaching best practices while showing demos that clearly violate the concepts I'm hypocritically proposing. Best practices are best provided by the community or better yet by an experienced solutions integrator (SI) partner. The truth is that best practices are not one-size-fits-all, and every environment may have different requirements.

As sustainability becomes increasingly important in tech, how are you integrating GreenOps practices into your DevOps workflows? What strategies are you using to optimise energy efficiency and reduce environmental impact while maintaining high performance?

Cloud providers have been responsive to customer needs on carbon footprint. Google for example has provided carbon data at the billing level for years. You can extract these values with Terraform or with API calls in your pipeline. I think a lot of people confuse cost with carbon footprint. Viewing energy costs around the globe reveals why US regions are usually cheaper than European regions. UK&I for example has some of the highest electricity costs but a lot of it is provided by renewable energy. Cross the channel to the Netherlands and costs are lower with widely available renewable energy and energy waste heat recapture meaning that cooling costs are not only lower but the energy is being used for good. We're also seeing a new wave of energy efficient chips landing in cloud regions. Intel chips still widely outperform solutions from ARM and RISC-V but performance per watt is drastically dropping off if your team is prepared for architecture flexibility.

What are some of the most common reasons DevOps initiatives fail, and what lessons have you learned from these failures that could help others avoid similar pitfalls?

If I were to identify the three main reasons for DevOps initiatives to fail, these would be:
a) budgeting: DevOps is a non-trivial, complex process that needs to be budgeted properly.
b) insufficient skills: Many people think that learning a tool or two is sufficient for devops. Unfortunately this is not the case. A DevOps engineer needs to be able to write new software if needed and not just rely on existing software to be successful.
c) cultural reasons. No matter how skilled your DevOps engineers are, if the company doesn't grow a DevOps culture and sticks on per-team responsibilities, then the initiative will probably fail.

Looking ahead, how do you envision the future of DevOps evolving over the next few years? What emerging technologies, practices, or cultural shifts do you believe will have the biggest impact on the way we approach DevOps

Firstly, I believe that the increasing adoption of cloud-native technologies such as serverless computing, containers, and microservices will continue to shape the way we approach software development and deployment. Secondly, I predict that the rise of artificial intelligence (AI) and machine learning (ML) will have a significant impact on DevOps practices, both by freeing up developers from repetitive and time-consuming tasks and for improving accuracy and efficiency of testing and debugging; all of these result in faster and more reliable software releases. Lastly, I believe that cultural shifts towards greater collaboration and transparency will also play a key role in shaping the future of DevOps. As software development becomes increasingly complex and interconnected, it is essential that teams work together effectively to share knowledge, skills, and resources. This requires a culture of openness, trust, and continuous learning, where everyone is empowered to contribute and innovate.

If you could automate one aspect of your job or daily routine - no matter how mundane or outrageous - what would it be and why?

I would automate spam control and meeting priority. Maybe it's just Microsoft Outlook 365 but I spend way too much time reading through spam and trying to sort out legitimate contacts and meeting requests. Infrastructure is easy by comparison.

To hear John's insights compared with 7 other industry leaders, download DevOps Journeys 4.0 today. DevOps Journeys provides a roadmap to navigate evolving challenges and stay ahead of the curve.

Whether you’re advancing your DevOps skills or initiating digital transformation, this resource is invaluable for every DevOps enthusiast.

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