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Docker Orchestration with Compose and Swarm

Course Code

CT04

Duration

2 Days

Docker Foundation or equivalent experience. Each participant will be required to run a 64 bit virtual machine (provided with the course).
The course begins with an introduction to Compose and its purpose, followed by coverage of the entire Compose DSL, giving participants a comprehensive look at the container configuration possibilities in Compose. The course also discusses the strengths and weaknesses of Compose in a production setting, including its integration with Docker Swarm and its use of Docker Stacks. The Swarm section of the course is designed to give Docker users a comprehensive introduction to container orchestration with Docker Swarm, covering Swarm architecture, types of cluster nodes and node configurations and introducing participants to managers and workers which use the Raft and Gossip protocols respectively. The experimental Distributed Application Bundle (DAB) image format is covered along with Docker stacks, services, tasks and other Docker Swarm primitives. Docker services are covered in depth, including replicated and global service operation, scaling features, task states, the Docker service routing mesh, service discovery and failover.
This course is designed for Developers, IT and QA Staff, Technical Managers, DevOps and Build personnel.

Upon completion of this course, participants will be able to:

  • Immediately begin using Compose and Swarm in development, test and production scenarios
Day 1 – Compose in Depth
Compose Overview - Installing Compose, basic Compose yaml and the Compose CLI
The Compose DSL Part I - Services, images and builds
The Compose DSL Part II - Volumes and networks
Compose in Production - Building larger applications and working with container clusters

Day 2 – Swarm in Depth
Swarm Overview - Managers, nodes, setup, and configuration
Swarm Services - Services, tasks, scaling and deployment
Swarm Networking - Multihost networking and the swarm routing mesh
Swarm in Production - Building larger clusters and working with advanced features
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