Читать книгу Google Cloud Certified Professional Cloud Architect Study Guide - Dan Sullivan - Страница 75

There are several things to consider about how data is processed. These include the following:

 Distance between the location of stored data and services that will process the data

 Volume of data that is moved from storage to processing services

 Acceptable latency when reading and writing the data

 Stream or batch processing

 In the case of stream processing, how long to wait for late arriving data

The distance between the location of data and where it is processed is an important consideration. This affects both the time it takes to read and write the data as well as, in some cases, the network costs for transmitting the data. If data will be accessed from multiple geographic regions, consider using multiregional storage when using Cloud Storage. If you are storing data in a relational database, consider replicating data to a read-only replica located in a region closer to where the data will be read. If there is a single write instance of the database, then this approach will not improve the time to ingest or update the data.

Understand if the data will be processed in batches or as a stream. Batch processes tend to tolerate longer latencies, so moving data between regions may not create problems for meeting business requirements around how fast the data needs to be processed. If data is now being processed in batch but it will be processed as a stream in the future, consider using Cloud Dataflow, Google's managed Apache Beam service. Apache Beam provides a unified processing model for batch and stream processing.

When working with stream processing, you should consider how you will deal with late arriving and missing data. It is a common practice in stream processing to assume that no data older than a specified time will arrive. For example, if a process collects telemetry data from sensors every minute, a process may wait up to five minutes for data. If the data has not arrived by then, the process assumes that it will never arrive.

A common architecture pattern is to consume data asynchronously by having data producers write data to a Cloud Pub/Sub topic and then having data consumers read from that topic. This helps prevent data loss when consumers cannot keep up with producers. Asynchronous data consumption also enables higher degrees of parallelism, which promotes scalability.

Business requirements help shape the context for systems integration and data management. They also impose constraints on acceptable solutions. In the context of the exam, business requirements may infer technical requirements that can help you identify the correct answer to a question.