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.”
Every time we look at data analytics and data systems, the idea of having a way to manage control and explain the data is actually as important as the data itself. This episode is all about metadata, specifically, metadata related to data analytics analysis, Big Data computation, sort of the data lake metadata problem.
Today we discuss the challenges of data management, but also the potential of understanding so much more about how data is used. If you are an IT professional or a data professional, you will find this conversation about how we’re going to draw inferences, manage and control all of that data that were collected fascinating.
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.
We check in on data gravity to see how generative AI and conversations about metadata and thinking on data lakes impacts data gravity thinking in general.
Data gravity is a concept that has been propagated by David McCrory, a friend of mine, who defined this idea that data itself, the aggregation of data, the use and transit of data has a gravitational effect. That it pulls more data to it as well as workloads towards it.
We jumped right into impacts of data gravity in this conversation.
Is hardware going to be innovative and change? Brian Cantrell brings up oxide computing and some of their design motivation.
Today we discuss our skepticism about some of his points, as well as the impacts for cloud distributed Compute hardware design mainframes, cloud, repatriation, and a whole bunch of topics about next generation thinking in Compute infrastructure management and applications.
We are officially starting our cloud2030 book group and I hope you will join us – we are going to be reading Data Cartels by Sara Landon, followed by Investments Unlimited by John Willis and crew.
How does AI chat and generative AI have the potential to disrupt everything we know about social media? Today we talk Twitter versus mastodon.
We spend most of our time talking about the power, influence and simple use cases for generative AI.
Is this going to break Mastodon, Twitter and other forms of social media? We have a pretty compelling conversation about that, too.
If you’re a fan of Mastodon and Twitter, jump forward to about 30 minutes in when we really start getting down to that topic. Stay tuned for our agenda as a bonus extra in the back half of the podcast.
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.
Is platform engineering effective at hiding complexity from developers? Today we tear apart what platform engineering is doing, how it came about and what it’s trying to be.
We discuss what companies are trying to accomplish with platform engineering – how can successful efforts improve outcomes for development teams and operations teams by improving collaboration in contracts? Why and how is that important, and what do those efforts entail?
In the Cloud 2030 podcast episode on platform engineering, Rob Hirschfeld, CEO of RackN, explores the profound impact of platform engineering on operational efficiency and developer complexity. He emphasizes the discipline’s role in making operations accessible, efficient, and repeatable, leading to significant benefits for companies and DevOps teams. While discussing how platform engineering can hide complexity, Hirschfeld highlights the ongoing essential work involved, debunking the notion of shortcuts and emphasizing the value it adds to organizations. He invites listeners to join discussions at the 2030.cloud, where important technology topics are analyzed in depth.
How can the intersection of generative AI machine learning and artificial intelligence be applied to environments using digital twins? Today we discuss digital twins and artificial intelligence.
How can we improve the simulations, the systems, the interactions that we build? How can we correctly model complex components of everything from cars to pumps in ways that allow us to then build on top and build more intelligent systems.
In the Cloud 2030 podcast episode on digital twins and AI, Rob Hirschfeld discusses the potential of using digital twins in handling real-world disasters, citing the recent train derailment in Ohio as an example. The concept involves quickly creating a digital twin of a disaster space to enable robots to learn, adapt, and efficiently mitigate the situation. Hirschfeld emphasizes the unprecedented opportunities for improving environmental interactions, responding to crises, and highlights the sophistication of ideas discussed in the episode. He encourages listeners to explore the full conversation on digital twins and AI at the2030.cloud.