Shared Kubernetes practices for product and data teams

We offer private, instructor-led training for product, analytics, and infrastructure engineers who use shared Kubernetes-backed data services and workloads.

The groups need common definitions of successful execution, available data, and operating responsibility. A shared platform must preserve the different needs of request-serving, batch, and stateful work.

LearnKube uses a reference service with a bounded task and a data dependency. Participants compare lifecycle assumptions and review which requirements belong to the platform or workload owner.

Discuss your team's training

Hands-on learning and the skills engineers take back to work.

Preview: course-wide figures are not yet available.

  • Hands-on learning
    60%
    Of instruction time spent on labs and challenges.
  • Troubleshooting confidence
    86%
    Of respondents report greater confidence diagnosing Kubernetes problems.
  • Relevant to your work
    92%
    Of respondents say the course addressed their engineering responsibilities.
  • Skills put into practice
    95%
    Of respondents applied their new skills at work within 90 days.

Agree on the contract while preserving workload differences

  • Use a shared lifecycle model by comparing service availability with task completion and persistent state, so teams interpret success according to the workload.
  • Apply common dependency conventions by reviewing data access, configuration, and recovery expectations, so different execution models have explicit requirements.
  • Clarify data-platform ownership by tracing a workload's interaction with a shared service, so each group knows which behavior it supplies or must verify.
  • Review the reference application together by comparing its components against shared criteria, so legitimate differences between product and analytics work remain visible.

Common criteria must recognize different execution models

Product and data teams can share infrastructure while expecting different workload behavior. A Job manages execution toward completion, while a StatefulSet maintains identity for stateful Pods. Neither defines the application's complete data contract. The workshop compares those mechanisms with service availability so teams can agree on success, recovery, and ownership without forcing every component into one model.

I ask what success means for each component before the groups choose a common review. A ready service, a completed task, and available storage are useful signals, but they do not establish the same application outcome.

— Daniele Polencic, LearnKube founder and Kubernetes instructor

Decision to alignCommon basisShared exercise
Successful executionReadiness and task completionCompare workload outcomes
Persistent stateIdentity and storage lifecycleReview data assumptions
Dependency accessCredentials and connectivityTrace a shared-service request
Recovery ownershipPlatform and application dutiesCompare recovery responsibilities

Your team is not alone in defining shared data-platform practices

Collibra's platform work supports shared data services and Kubernetes/GitOps infrastructure. Infrastructure, product, analytics, security, and SRE groups collaborate on the adoption of platform practices.

For teams sharing those services, we recommend a four-day workshop focused on the Kubernetes contract and the responsibilities around it. The examples compare service, task, and persistent-data expectations so the groups can review their differences explicitly.

A recommended four-day workshop around a service and data task

This proposed agenda follows a small reference application with request-serving, task, and data requirements. Participants review the components as related work with different lifecycles.

Day 1

We connect images, Deployments, Services, Jobs, and health checks to the reference application. Product and data roles compare the evidence that establishes availability or completion.

Hands-on exercises

  • Run a service and a bounded task, then compare what their status establishes.
  • Review the application-specific evidence each group needs before declaring success.

Day 2

We use Helm and compare Kustomize to inspect the supporting resources. Participants connect controllers and cluster components to the responsibilities of workload and shared-service owners.

Hands-on exercises

  • Trace configuration and data dependencies across the reference components.
  • Compare an application retry decision with a controller's replacement behavior.

Day 3

We trace DNS, Services, ingress, network policies, and placement through the data path. Mesh-related controls are considered where they affect the same connection requirements.

Hands-on exercises

  • Compare permitted access to the shared data dependency from both workload types.
  • Review resource or placement differences that follow from their execution models.

Day 4

We examine storage, secrets, autoscaling signals, and RBAC in the application contract. Participants distinguish the scale mechanism for a service from task demand and data-service capacity.

Hands-on exercises

  • Review which component owns durable output, recovery, and access verification.
  • Present a joint assessment that preserves the legitimate differences between the workloads.

Your product and data groups, shared services, and responsibility boundaries can shape the agenda. Get in touch to tailor the workshop to how your teams work together.

Ask whether the groups mean the same outcome

When one team treats a completed task as equivalent to a healthy service, the instructor can compare their lifecycle conditions and data effects. The group can then choose review questions that fit each component instead of forcing a single status check.

Asked what she liked most about the course, Arpitha wrote:

Labs, Greg's examples during the session and the Quiz

— Arpitha, Application Developer at Baillie Gifford.

Identify the data-platform contracts that need a common meaning

Tell us which product and data teams share Kubernetes services, which workload expectations need clarification, and how ownership differs. We will recommend shared examples and role-specific review exercises.

Discuss your team's training