Google Cloud Certified Professional Cloud Architect Study Guide. Dan Sullivan
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СКАЧАТЬ an application may collect performance metrics every minute. After three days, there is no need to query to the minute level of detail, and data can be aggregated to the hour level. After one month, data can be aggregated to the day level. This incremental aggregation will save space and improve response times for queries that span large time ranges.

       Object storage is often used for unstructured data and backups. Standard Storage class should be used for frequently accessed data. If data is accessed at most once a month, then Nearline storage can be used. When data is not likely to be accessed more than once in 90 days, then Coldline storage should be used. Archive storage is appropriate for objects that are not accessed more than once per year.

      Lifecycle conditions can be based on the following:

       The age of an object

       When it was created, including CreatedBefore and CustomTimeBefore conditions

       Days since a custom time metadata field on an object

       The object's storage class

       The number of versions of an object as well as the number of days since the object became noncurrent

       Whether or not the object is “live” (an object in nonversions bucketed is “live”; archived objects are not live)

       Storage class

      You can monitor data lifecycle management either by using Cloud Storage usage logs or by enabling Pub/Sub notifications for Cloud Storage buckets. The latter will send a message to a Pub/Sub topic when an action occurs.

      Business requirements can give information that is useful for identifying dependencies between systems and how data will flow through those systems.

      Systems Integration Business Requirements

      One of an architect's responsibilities is to ensure that systems work together. Business requirements will not specify technical details about how applications should function together, but they will state what needs to happen to data or what functions need to be available to users.

      Let's review examples of systems integration considerations in the case studies. These are representative examples of system integration considerations; it is not an exhaustive list.

      EHR Healthcare Systems Integration

      Even though the existing systems will not be migrated, new cloud-native systems will be developed. As an architect working on that project, you would consider several challenges, including the following:

       Understanding the volume and types of data exchanged

       Deciding how to authenticate service requests

       Encrypting data at rest and in transit

       Managing encryption keys

       Decoupling services to accommodate spikes in service demand

       Designing ingestion and data pipelines

       Monitoring and logging for service performance as well as security

       Using multiregion storage and compute resources for high availability while operating within any regulations that put constraints on where data may be stored and processed

      In addition to these technical design issues, the architect and business sponsors will need to determine how to retire existing on-premises systems while bringing the new systems online without disrupting services.

      Helicopter Racing League

      The Helicopter Racing League is highly focused on improving predictive analytics and integrating their findings with the viewer platform. Consider two types of analytics described in the case study: (1) viewer consumption patterns and engagement and (2) race predictions.

      To understand viewer consumption patterns and engagement, the company will need to collect details about viewer behaviors during races. This will likely require ingestion systems that can scale to large volumes of data distributed over a wide geographic area. The ingestion system will likely feed a streaming analysis data pipeline (Cloud Dataflow would be a good option for this service), and the results of the initial analysis as well as telemetry data may be stored for further analysis.

      In fact, the data may be stored in two different systems for further analysis. BigQuery is optimized for scanning large volumes of data and would make it a good choice for analyzing data that spans a race or multiple races and entails hundreds of terabytes of data. Bigtable provides low-latency writes and is highly performant for key-based lookups and small scans, such as time-series data for a single viewer over the past 10 minutes.

      Mountkirk Games Systems Integration

      Let's consider how datastores and microservices architectures can influence systems integration.

      Mountkirk Games uses a microservices architecture. Microservices are single services that implement one single function of an application, such as storing player data or recording player actions. An aggregation of microservices implements an application. Microservices make their functions accessible through application programming interfaces (APIs). Depending on security requirements, services may require that calls to their API functions are authenticated. High-risk services, such as a payment service, may require more security controls than other services. Cloud Endpoints may help to manage APIs and help secure and monitor calls to microservices.

      TerramEarth Systems Integration

      From the description of the current system, we can see that on-board applications СКАЧАТЬ