Installing Kubernetes is easy. Ensuring it complies with your organization’s enterprise governance and security requirements aren’t. Oleg will outline a plan to use the technology while meeting enterprise security requirements. In this technically-focused talk, he’ll summarize common prerequisites for running Kubernetes in production, and how to leverage fine-grained controls and separation of responsibilities to meet enterprise governance and security needs.

In a microservices world, applications consist of dozens, hundreds, or even thousands of components. Manually deploying and verifying deployment quality in production is virtually impossible. Kubernetes, which natively supports rolling updates, enables blue-green application deployments with Spinnaker. However, the gradual rollout is a feature that doesn't come out-of-the-box but can be achieved by adding Istio and Prometheus to the equation.

While developers see and realize the benefits of Kubernetes, how it improves efficiencies, saves time, and enables focus on the unique business requirements of each project; InfoSec, infrastructure, and software operations teams still face challenges when managing a new set of tools and technologies, and integrating them into existing enterprise infrastructure. 

Digital transformation may be in danger of becoming an overused buzzword. Yet, real business needs are driving this trend and IT leaders feel the pressure to transform their businesses every day. Whether it is the need for speed, agility, or rethinking business processes as a whole – these challenges are here to stay.

Kubernetes ensures your deployed applications are always available to users. But how do you deploy applications in Kubernetes without user/service interruptions? Should you write your own scripts using low-level Kubernetes objects, package everything in Helm, or use specific CI/CD tools? There isn’t a clear-cut answer; it always depends.

Enabling support for data processing, data analytics, and machine learning workloads in Kubernetes has been one of the goals of the open-source community. During this meetup, we’ll discuss the growing use of Kubernetes for data science and machine learning workloads. We’ll examine how new Kubernetes extensibility features such as custom resources and custom controllers are used for applications and frameworks integration. Apache Spark 2.3.’s native support is the latest indication of this growing trend. We’ll demo a few examples of data science workloads running on Kubernetes clusters setup by our Kublr platform.

Is advanced scheduling in Kubernetes achievable? Yes, however, how do you properly accommodate every real-life scenario that a Kubernetes user might encounter? How do you leverage advanced scheduling techniques to shape and describe each scenario in easy-to-use rules and configurations?

Containers and Kubernetes allow for code portability across on-premise VMs, bare metal or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this online meetup Oleg Chunikhin, CTO at Kublr, describes best practices for “configuration as code” in a Kubernetes environment. He demonstrates how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and services, can be used to abstract from the infrastructure.