Google Professional Cloud Architect Certification: 2. Designing for Technical Requirements - Important Things To Know

Cloud Architects are constantly working with technical requirements. Sometimes these requirements are explicitly stated, such as when a line-of-business manager states that the system will need to store 10 TB of data per day or that the data warehouse must support SQL. In other cases, you have to infer technical requirements from other statements. If a streaming application must be able to accept late-arriving data, this implies the need to buffer data when it arrives and to specify how long to wait for late data.
Some technical requirements are statements of constraints, like requiring that a database be implemented using MySQL 5.7. Other technical requirements require architects to analyze multiple business needs and other constraints in order to identify requirements. Many of these fall into the categories of high availability, scalability, and reliability. Compute, storage, and networking services should be designed to support the levels of availability, scalability, and reliability that the business requires.
 

1. Understand the differences between availability, scalability, and reliability. High availability is the continuous operation of a system at sufficient capacity to meet the demands of ongoing workloads. Availability is usually measured as a percentage of time that a system is available. Scalability is the process of adding and removing infrastructure resources to meet workload demands efficiently. Reliability is a measure of how likely it is that a system will be available and capable of meeting the needs of the load on the system.
2. Understand how redundancy is used to improve availability. Compute, storage, and network services all use redundancy to improve availability. Clusters of identically configured VMs behind a load balancer is an example of using redundancy to improve availability. Making multiple copies of data is an example of redundancy used to improve storage availability. Using multiple direct connections between a data center and Google Cloud is an example of redundancy in networking.
3. Know that managed services relieve users of many responsibilities for availability and scalability. Managed services in GCP take care of most aspects of availability and scalability. For example, Cloud Storage is highly available and scalable, but users of the service do not have to do anything to enable these capabilities.
4. Understand how Compute Engine and Kubernetes Engine achieve high availability and scalability. Compute Engine uses managed instance groups, which include instance templates and autoscalers, to achieve high availability and scale to meet application load. Kubernetes is a container orchestration service that provides higher-level abstractions for deploying applications on containers. Pods scale as needed to meet demands on the cluster and on application services.
5. Understand reliability engineering is about managing risk. Designing for reliability requires you to consider how to minimize the chance of system failures. For example, architects employ redundancy to mitigate the risk of a hardware failure leaving a crucial component unavailable. Rather than focus on the implementation metrics, such as number of instances available, reliability is better measured as a function of the work performed by the service. The number of requests that are successfully responded to is a good basis for measuring reliability.