We talk about DataOps, but if you’re expecting this to be DevOps for data – you are mistaken. Today we talk about engineering data through the idea of data stewardship or how you manage and control the data.
Beyond permissions and access into the costs and how things are stewarded, how logs are handled, who controls how much access and how quickly people have access, where you’re putting the data to improve its effectiveness, and more. There is so much going on above the infrastructure, but below that actual analytics, this conversation will open your mind to a whole layer of challenges related to governing and managing data.
This is the second installment of our book group, which is a discussion about Investments Unlimited. We have one of our authors, and a great all around DevOps enthusiast, John Willis, on the call with us.
As you might expect, while we talk about the book and John gives a lot of background and details about the book, we treat it with the classic cloud2030 style, and bring in AI, large language and advanced DevOps.
We take the topics of the book to the next level, and frame it in the moment of the year, looking beyond and into how the concepts of compliance, validation, team coordination and risk assessment are incorporated into the coming AI and how it changes in our landscape.
NOTE: This is Rob’s Gluecon topic on 5/24. Save $300 if you register with speaker300 at www.gluecon.com
We dive into the question of whether or not generative AI can be used to productively change DevOps automation and the control of infrastructure.
We’ve discussed the closed loop side of using AI to manage infrastructure in the past, but this episode we really dive into the idea of creating automation and using generative AI.
Today we look at what it takes to have much more collaborative building of automation, templates and shared components that are necessary to really drive platform engineering, and not just between teams at the same company.
We make components for infrastructure automation that bridges the industry because they can be shared much more broadly, similar to the way we share modules in coding languages. We dug into what it takes to make that type of environment work in automation, and what are the prerequisites of the environment?
How has Kubernetes changed our industry? Today’s discussion is part of a multi podcast conversation in which we’re going to think about ways in which Kubernetes could go away, or could influence other technologies in such a way to be transformative.
We went down the path of what we have learned from Kubernetes and how it influences other aspects of IT operations, architecture and design, and explored the impact that the expectation for declarative immutable operational constructs will play into other aspects of our system. We also discuss micro LS microkernels and how operations are staged to talk about the need for declarative OS, banking on this idea that what Kubernetes has built extends into other areas.
Chat GPT Summary: “The conversation is part of a multi-podcast series focused on exploring ways in which Kubernetes could influence other technologies, as well as the potential consequences if it were to disappear. During the discussion, the group delved into the lessons learned from Kubernetes and its impact on various aspects of IT operations, architecture, and design. One key takeaway was the importance of declarative immutable constructs in managing the complexities of modern IT systems. The group also explored the potential for microkernels to revolutionize system design and emphasized the need for declarative operating systems. Overall, the discussion highlighted the transformative role that Kubernetes has played in shaping the IT industry and underscored the importance of adopting a declarative, immutable approach to managing complex IT systems.”
Emily Friedman’s DevOpsDays Ukraine presentation about rethinking the software development lifecycle or SDLC sparks our conversation today. She describes looking at it as a multi-dimensional cross functional discipline, that actually accounts for six different vectors of capabilities that need to be factored in – a resilient and robust look at the SDLC. Watch her YouTube:
We found that the model does not cover all of the things that we’ve been discussing as important things to consider in building, deploying, and making software resilient and reliable, most specifically software, build materials, or s bombs.
What are the human and management factors that go into building great platform engineering? And what are the efforts of control having too much control or too much flexibility, not enough collaboration, not creating space for innovation, and changing inside what’s inside these platform engineering efforts?
Today, we discuss centralized versus decentralized platform engineering, or as came up in the conversation about platform engineering, it’s the opposite of Java Enterprise, version and platform.
As you’re doing this type of work interacting with platform teams should influence how you design and authorize the effort to make that work. What type of slack you need to put in the system and what type of authority needs to be given to the platform engineering team.
In the Cloud 2030 Podcast episode on March 14th, Rob Hirschfeld discusses the importance of adopting a system-wide view in platform engineering, emphasizing the need to identify over-optimization in certain areas like developer productivity while underestimating other critical aspects such as operations, security, or compliance. Hirschfeld advocates for a holistic approach to platform engineering, focusing on optimizing the entire system, streamlining teams, and making strategic trade-offs rather than just emphasizing technology or developer productivity. He suggests that this mindset can lead to improved efficiency, productivity, and return on investment for platform teams, highlighting the significance of considering the broader organizational context. Hirschfeld encourages listeners to explore the March 14 episode for a deeper understanding of these concepts, available on the 2030.cloud platform.
What is generative AI and what are people now just generically calling ChatGPT?
We put these things in a technical frame, meaning can we use generative AI to improve our programming, testing or automation? What does it take to use these concepts in ways that iteratively improve IT infrastructures.
We review the state of chat, ChatGPT, AI infrastructure and things like that.
In a discussion on the DevOps Lunch and Learn podcast, Rob Hirschfeld, CEO of RackN, explores the complexities of generative AI and its impact on coding and automation. Hirschfeld raises questions about trust in generative AI models, emphasizing the need to understand how they are trained, updated, and refined to eliminate errors. He highlights the importance of creating reliable training sets to ensure the technology’s applications, focusing on enhancing system resilience and maintainability.
We break down the edge compute cluster by the Chick-fil-A team, and we talk about how they use Kubernetes, specifically K3s in 2500 of their restaurants to build an IoT and restaurant management system. This system uses Intel Knucks, a commodity commercial residential grade hardware.
It’s an update on a four year old Kubernetes story with a lot of buzz, and they show how they have been successful building this system.
If you’re interested in Kubernetes, Edge DevOps and distributed systems, this episode has a lot to enjoy.
In the Cloud 2030 podcast episode on Chick-fil-A’s Kubernetes control plane, Rob Hirschfeld highlights the challenges and benefits of transitioning cloud infrastructure and applications to edge locations using commodity gear. He emphasizes the success of Chick-fil-A’s approach in bringing cloud tools and platforms to non-cloud environments, showcasing the potential for mapping cloud processes back into edge computing. Hirschfeld encourages listeners to explore the detailed discussion on Chick-fil-A’s edge clusters and engage in broader conversations on Cloud 2030 at the2030.cloud.
How do we improve the time to decision for CIOs? Today we talk about general business practice and how we can help.
Technical innovators and architects create value for the teams that they support. This can either be from an automation perspective, which is what RackN does, or from a data perspective, which Tyler describes in the podcast today. These are real challenges.
When we flip the script and talk about the miscommunication between how CIOs see business challenges and translate business challenges into technical delivery, however, we get into a fascinating set of discussions where Joanne brings up some key challenges for 2023. We also discuss mapping those from CIOs into implementation, followed by ultimately trying to find ways that people can make fast, valuable decisions that feel right.
This conversation is rooted in important conceptual executive level thinking. We’ll include the list of points that Joanne talks about below as well as in the show notes, and I highly recommend you check those out.
Joanne Friedman’s Key Challenges for 2023 1. Tackle inflation and margin pressure 2. Migrate supply chain disruption 3. Make sustainability a pillar 4. Calibrate talent management strategy 5, Streamline procurement and sourcing 6. Strengthen digital thread 7. Prioritize innovation initiatives
We try to name these key challenges, but in a lot of cases we are talking about the list or the graphic. You’ll need to refer to the show notes in order to see the complete one.
In the Cloud 2030 podcast episode dated February 9, CEO Rob Hirschfeld discusses the challenge of making fast, impactful decisions in a rapidly changing technological landscape. He emphasizes the importance of intuitive understanding and emotional context in decision-making, highlighting the need for clearly articulated goals and consensus building beforehand. Hirschfeld advocates for executives to rely on their intuition, grounded in a solid foundation of groundwork, and invites listeners to engage in ongoing discussions on this topic at 2030.Cloud.
