Google Cloud Certified Professional Cloud Architect Study Guide. Dan Sullivan

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СКАЧАТЬ Budget A budget allocating funds to meet the costs of labor, supplies, and other expenses related to performing the day-to-day operations of a business. Contrast this to capital expenditure budgets, which are used for longer-term investments.

       Service-Level Agreement (SLA) An agreement between a provider of a service and a customer using the service. SLAs define responsibilities for delivering a service and consequences when responsibilities are not met.

       Service-Level Indicator (SLI) A metric that reflects how well a service-level objective is being met. Examples include latency, throughput, and error rate.

       Service-Level Objective (SLO) An agreed-upon target for a measurable attribute of a service that is specified in a service-level agreement.

Analyzing Technical Requirements

      Technical requirements specify features of a system that relate to functional and nonfunctional performance. Functional features include providing Atomicity, Consistency, Reliability, and Durability (ACID) transactions in a database, which guarantees that transactions are atomic, consistent, isolated, and durable; ensuring at least once delivery in a messaging system; and encrypting data at rest. Nonfunctional features are the general features of a system, including scalability, reliability, observability, and maintainability.

      Functional Requirements

      The exam will require you to understand functional requirements related to computing, storage, and networking. The following are some examples of the kinds of issues you will be asked about on the exam.

      Understanding Compute Requirements

      Google Cloud has a variety of computing services, including Compute Engine, App Engine, Cloud Functions, Cloud Run, and Kubernetes Engine. As an architect, you should be able to determine when each of these platforms is the best option for a use case. For example, if there is a technical requirement to use a virtual machine running a particular hardened version of Linux, then Compute Engine is the best option. Sometimes, though, the choice is not so obvious.

      If you want to run containers in a managed service on Google Cloud Platform (GCP), you could choose from App Engine Flexible, Cloud Run, or Kubernetes Engine. If you already have application code running in App Engine and you intend to run a small number of containers, then App Engine Flexible is a good option. If you plan to deploy and manage a large number of containers and want to use a service mesh like Anthos Service Mesh to secure and monitor microservices, Kubernetes Engine is a better option. If you are running stateless containers that do not require Kubernetes features such as namespaces or node allocation and management features, then Cloud Run is a good option.

      Understanding Storage Requirements

      There are even more options when it comes to storage. There are several factors to consider when choosing a storage option, including how the data is structured, how it will be accessed and updated, and for how long it will be stored.

      Let's look at how you might decide which data storage service to use given a set of requirements. Structured data fits well with both relational and NoSQL databases. If SQL is required, then your choices are Cloud SQL, Spanner, BigQuery, or running a relational database yourself in Compute Engine. If you require a global, strongly consistent transactional data store, then Spanner is the best choice, while Cloud SQL is a good choice for regional-scale databases. If the application using the database requires a flexible schema, then you should consider NoSQL options. Cloud Firestore is a good option when a document store is needed, while Bigtable is well suited for ingesting large volumes of data at low latency.

Of course, you could run a NoSQL database in Compute Engine. If a service needs to ingest time-series data at low latency and one of the business requirements is to maximize the use of managed services, then Bigtable should be used. If there is no requirement to use managed services, you might consider deploying Cassandra to a cluster in Compute Engine. This would be a better choice, for example, if you are planning a lift-and-shift migration to the cloud and are currently running Cassandra in an on-premises data center.

      When long-term archival storage is required, then Cloud Storage is the best option. Since Cloud Storage has several classes to choose from, you will have to consider access patterns and reliability requirements when choosing a storage class. If the data is frequently accessed, Standard Storage class storage is appropriate. If high availability of access to the data is a concern or if data will be accessed from different areas of the world, you should consider multiregional or dual-region storage. If data will be infrequently accessed, then Nearline, Coldline, or Archive storage is a good choice. Nearline storage is designed for data that won't be accessed more than once a month and will be stored at least 30 days. Coldline storage is used for data that is stored at least 90 days and accessed no more than once every three months. Archive storage is well suited for data that will be accessed not more than once a year. Nearline, Coldline, and Archive storage have slightly lower availability than Standard Storage.

      Understanding Network Requirements

      Networking topics that require an architect tend to fall into two categories: structuring virtual private clouds and supporting hybrid cloud computing.

      Virtual private clouds (VPCs) isolate a Google Cloud Platform customer's resource. Architects should know how to configure VPCs to meet requirements about who can access specific resources, the kinds of traffic allowed in or out of the network, and communications between VPCs. To develop solutions to these high-level requirements, architects need to understand basic networking components such as the following:

       Firewalls and firewall rules

       Domain name services (DNS)

       CIDR blocks and IP addressing

       Autogenerated and custom subnets

       VPC peering

      Many companies and organizations adopting cloud computing also have their own data centers. Architects need to understand options for networking between on-premises data centers and the Google Cloud Platform network. Options include using a virtual private network (VPN), Dedicated Interconnect, and Partner Interconnects.

      Virtual private networks are a good choice when bandwidth demands are not high and data is allowed to traverse the public Internet.

      Dedicated Interconnects are used when a 10 Gbps connection is needed and both your on-premises point of presence and a Google point of presence are in the same physical location.

If you do not have point of presence co-located with a Google point of presence, a Partner Interconnect can be used. In that case, you would provision a connection between your point-of-presence location and a Google point of presence using the telecommunications partner's equipment.

      Nonfunctional Requirements

      Nonfunctional requirements often follow from business requirements. They include the following:

       Availability

       Reliability

       Scalability

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