Simon Brown talks to Stefan Tilkov about software architecture – the importance for developers, its role in agile software development, documentation and tooling, and the importance (or lack of importance) of UML.
Eoin Woods discusses with Sven Johann all the things developers need to know to bring systems successfully into production and how to keep them there. They discuss production environments, what goes wrong in production, architectural requirements for operations, cost of very high availability, stability and capacity, communicating operational concerns, observability, learning from incidents, chaos engineering and operational models (SRE, You build it, you run it, classic).
In this episode, Stefan talks to software developer Christin Gorman about her experience with very large software projects and the reasons why they fail, especially in the public sector. The two address issues of size, approaches that do and don’t work, share some of their favorite failure stories and try to enumerate some strategies to do something about them.
In this episode, Artur Ortega explains to Stefan Tilkov what GraphQL is, how it came to be, and how it differs from REST. Predictably, the two debate whether it’s better or not, but also spend some time going over its technical details and applicability to different usage scenarios.
In this episode, Markus Völter talks with Stefan Tilkov about language engineering and its connection to domain-driven design. Picking up from their past interactions in various different podcasts, the two agree and disagree fundamentally in equal parts, on topics such as the usefulness of DSLs, the costs and benefits of domain experts writing code, and the tradeoffs of various architectural approaches.
Birgitta Böckeler talks with Sven Johann about cultivating architecture principles. They first discuss what architecture principles are, why they are useful, how they are structured and how they help to guide decision making across multiple teams. They then talk about their lifecycle: who creates them, checks if they are aligned with business goals, prioritizes them, how they should be communicated, how you know they are useful and when to potentially retire them.
Joy Heron talks with Rachel Andrew about CSS Layout. Rachel shares her journey as a web developer and how the web has changed over that time. They discuss Rachel’s work with the CSS working group and how developers can get involved to improve the web for the future. Rachel also talks about the current status of CSS layout and what we can expect in the near future. To wrap up, Rachel shares some practical tips and they discuss the future of the web platform.
Daniel Bryant talks with Sven Johann about the business problems to modernize applications. They need to be decoupled from the compute fabric and the network fabric and Daniel explains the reasons for that, what products are available and what strategies the different cloud vendors follow. They move on with how to get started, questions to select an API gateway, technologies behind them and the challenges. They talk about service meshes features, especially on the canary testing side, how they work under the hood and how they help as infrastructure with application modernization. They close the discussion on how to select a service mesh product and what rollout strategies exist.
Does functional programming matter? Stefan and Mike continue an interesting conversation they had on Twitter: Does the style of programming matter if you look at it from a larger distance? Mike describes two distinguishing characteristics of functional programming: Immutable data and high-level models, and how they influence the architecture of your application.
Peter Elger talks with Sven Johann about AI as a service - commodity web services offered by large cloud providers, which make it very easy to use AI in your application just by using an API. They start the discussion with the typical AI use cases companies in finance, retail and so on have and which of those are already as a commodity available and which are not. They then discuss how those APIs are used, from very simple API calls like text-to-speech to slightly more complicated ones like chat bots or recommendation engines. They continue with understanding how you come up with AI requirements and how to (regression) test your AI service, which is often different than doing the same with non-AI code. They clarify how much AI you need to know to use those services, the cost models of them and how to get your AI to production. The conversation then moves to lower level AI services like AWS SageMaker or Google Tensorflow and how you combine them to create novel pieces of AI relatively quickly. They close the conversation with when to use specific packaged AI solutions and when to create your own AI to push the envelope.