Kubernetes Podcast Episodes

Tobi Knaup is the CEO and a co-founder at D2iQ, an enterprise Kubernetes platform. D2iQ combines the best open-source technology from the cloud-native technology landscape into a single Kubernetes solution. Customers can deploy this solution without worrying about the individual pieces they would otherwise need to assemble, maintain, and update.

In this episode, we discuss the shift to cloud-native infrastructure and how we are now seeing a new class of smart cloud-native applications emerge. Smart cloud-native applications include artificial intelligence (AI) components that leverage data from production applications. These new applications enable entirely new use cases in every industry. Examples are autonomous driving in automotive, medical imaging in healthcare, and fraud detection in banking or crypto trading.

To build smart cloud-native applications, companies need to build the infrastructure to train their AI models, put them into production, and build differentiated products. It is an entirely new type of workload, with very dynamic and elastic demand for compute and storage.

It turns out that Kubernetes, with its scheduling and orchestration capabilities, is a perfect fit to support workloads from smart cloud-native applications. Training models requires spinning up large amounts of compute to process data and then scaling back down. By putting model predictions into production, companies can lean on existing code pipeline workflows and monitoring.

This also means that instead of running two separate types of infrastructure, companies can consolidate and run their smart cloud-native applications on the same platforms as their production applications, which generate the data in the first place. The outcome for companies is highly differentiated digital products.

Listen to this episode to learn about Kubernetes, cloud-native architecture, and changes in organization and workflows that technology leaders need to adapt to deliver smart cloud-native applications.

Jon Edvald is the founder and CEO of Garden, an end-to-end cloud delivery platform that accelerates your development, testing, and CI/CD workflows.

In this conversation, Jon covers how the shift from monolithic applications to microservices has taken us from a single codebase to individual deliverables that are getting smaller and smaller. With the introduction of containers, an application now consists of many discrete components—which continue to get even smaller with the arrival of serverless. And where teams previously had to manage five to ten codebases, they are now dealing with hundreds or even thousands. Testing and deploying these different codebases has become a graph problem.

Beyond adopting containers and Kubernetes, the complexity of that graph of system components pushes the boundaries of existing DevOps tool chains. There is overhead for setup of each component in the graph, which becomes unmanageable with existing tools.

Garden solves this issue by factoring out things that are undifferentiated across different teams, allowing them to focus on their own business problems. Garden builds a directed graph of everything that needs to happen to transition from a bunch of Git repositories to a fully built, deployed, and tested system.

Listen to this episode to learn more about the industrialization of continuous integration (CI), infrastructure as code (with popular tools like Terraform and Pulumi), and how Garden helps developers ship more software faster.