Course Overview
This course is a hands-on learning experience designed to provide an actionable set of steps to help your organizations implement a Cloud Financial Operations (FinOps) strategy to maximize your cloud investment with Google Cloud. This course focuses on the technical aspects of FinOps, and is suitable for Finance and/or IT engineering and operations roles.
Prerequisites
- BigQuery Basics for Data Analysts
- Analyzing and Visualizing Data with Looker (AVDL)
- Getting Started with Google Kubernetes Engine (GCP-GSGKE)
Course Objectives
- Retrieve recommendations for Google Resources using Recommender
- Create VMs in Premium and Standard network service tiers
- Compare VM latency and network paths
- Optimize your cloud network for performance and/or cost
- Learn how to get the best value from GKE Clusters
- Learn the four dimensions of auto-scaling in GKE
Outline: Getting Started with FinOps on Google Cloud Course - TechEng (GSFO-TE)
Course Introduction
Demonstration Lecture - Using Recommendations for Infrastructure as Code
Recommender enables you to retrieve recommendations for Google Cloud resources, helping you to improve cloud security, save costs, and more.
Eng Lab 1 - Optimizing Network Spend
In this lab, you create one VM in the Premium network service tier (default) and one VM in the Standard network service tier. Then you compare the latency and network paths for each VM instance. With Network Service Tiers, Google Cloud enables you to optimize your cloud network for performance by choosing the Premium Tier, or for cost with the new Standard Tier.
Understanding GKE Costs & Optimization
Eng Lab 2 - Exploring Cost-optimization for GKE
Eng Lab 3 - Understanding and Combining GKE Autoscaling Strategies
- Decrease number of replicas for a Deployment with Horizontal Pod Autoscaler
- Decrease CPU request of a Deployment with Vertical Pod Autoscaler
- Decrease number of nodes used in cluster with Cluster Autoscaler
- Automatically create an optimized node pool for workload with Node Auto Provisioning
- Test the auto scaling behavior against a spike in demand
- Overprovision your cluster with Pause Pods