Best Practice Configuration Management (Ansible/Fabric/Chef/...) Deployment/Continuous Integration and Delivery DevOps general Distributed Systems
Do you find easier to build a 3894 pieces IKEA’s furniture than using K8s?
Kubernetes 101 for Python Developers is here to help!
During this training we will discover how to use a GKE (Google Kubernetes Engine) cluster to deploy Python production ready applications using Kubernetes. After a theoretical introduction there will be hands on trainings units.
In the workshop we will cover:
Intro and recap about Docker and Kubernetes
- Brief recap about docker command (build, run, ps, logs) and Docker files
- Kurbernetes and Docker
- The Kubernetes architechture
- How to run kubernetes
Run a Kubernetes cluster using Terraform and GKE
- Intro to GKE
- Running your own cluster VS using GKE
- Why running your cluster inside GKE is a good idea
- Create&Destroy your first cluster using Terraform
Let’s run our application locally
- Introducing our application
- Structure of our application’s repository
- Running our application locally
Kubectl and our first deployment
- Kubectl, the Kubernetes CLI
- Imperative vs Declarative
- Use kubectl to introspect our cluster
- Run our application using kubectl (imperative)
- Run our application using kubectl and yaml (declarative)
- Exposing our application
- Volume and permanent data
- Multiple namespaces
- Metrics and monitoring
- Labels, taints and tolerations
- Tips on security
Type: Training (180 mins); Python level: Intermediate; Domain level: Intermediate
I am Senior Software Engineer, Tech Lead and speaker living in Berlin where I work at infarm, one of the top 10 startups in Berlin.
My focus is on microservices, event-driven architecture, devops for microservices and machine learning and data engineering.
I am an active member of the tech community in Berlin, organizer of PyCon DE and a Python Software Foundation Fellow.