Private cohort training for data engineering teams
This course teaches engineers how to inspect existing VM-based data jobs, package repeatable Docker images, read Kubernetes workload specs and plan handoffs between data pipelines and platform operations.
Route selector
The class can start from an infrastructure migration, a Docker packaging gap, or a Kubernetes handoff problem. The selector below changes the suggested lab route before the call.
Lab chain
Each station turns a familiar data engineering task into an operational decision: what stays on a VM, what belongs in an image, and what Kubernetes must own.
Teams list the jobs, volumes, environment variables, service accounts and upstream schedules that must survive a runtime change.
Course route
Inspect VM-hosted data jobs, dependencies, storage assumptions and scheduler behavior before packaging decisions are made.
Turn repeatable batch work into image build steps, runtime variables and local verification routines a team can maintain.
Read job, deployment and config patterns so data engineers can collaborate with platform teams without guessing at cluster behavior.
Draft the ownership map for logs, retries, secrets, data volumes and runbooks before the first production migration.
Tuition call prep
The course is quoted after scope because team size, current runtime and delivery format change the lab depth. Build a short phone brief locally in the browser; it is not submitted from this site.
Operator details
Training is coordinated through the course operator listed here. Call to confirm fit, delivery format and quoted tuition before any enrollment is scheduled.