Level: Intermediate to Advanced
Prerequisite: None
Many enterprises move workloads to the cloud hoping to save money, only to find that they don't. Careful planning and disciplined management are required to maximize cost efficiency.
It is common to exceed cloud spend budgets, and these overruns are often described as cost explosions and are attributed to surprise charges. Overruns are often connected with data warehousing, analytics, and increasingly, AI applications.
Many overruns are predictable and avoidable. In this course, students will learn how decisions made at every phase of software development have impacts on the ultimate cost of the solution. First, the choice of cloud service provider and cloud data platform have impacts on the cost of solutions that are ultimately built on these platforms. Second, decisions about solution architecture during the design phase also have important ramifications. Third, during operation of the built system, FinOps can be used to align cloud spend with business value.
Engineering is about balancing requirements. In efficiency engineering, we make the cost of running the solution a first-class metric of software performance, to be balanced with other metrics of software quality. By applying these principles in a disciplined manner, the improved business agility of operating in the cloud can be realized and alignment of cloud spending with business value can be maximized.
You Will Learn
- Which workloads should be run in the cloud, and what advantages result from using the cloud
- Key critical decisions at several phases of software development that impact cost
- To appreciate the magnitude of spending for analytics workloads and AI workloads
- Establishing budgets, forecasting, and attribution of cloud costs to cost centers
- The value of changing culture to bring about collaboration between engineering, finance, and business leaders
- How to create a shared responsibility model in your organization that aligns cloud spending with business value
Geared To
- Practitioners in cloud data warehouse and AI initiatives
- Engineers, developers, architects
- Finance managers
- Line-of-business leaders and product owners