Official Google Cloud Certified Professional Data Engineer Study Guide. Dan Sullivan
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the following:
1 Designing data processing systems✔ 1.1 Selecting the appropriate storage technologiesMapping storage systems to business requirementsData modelingTradeoffs involving latency, throughput, transactionsDistributed systemsSchema design
Another common use case is storing large volumes of data for batch processing, including using data to train machine learning models. Data engineers also consider the range of variety in the structure of data. Some data, like the kind found in online transaction processing, is highly structured and varies little from one datum to the next. Other data, like product descriptions in a product catalog, can have a varying set of attributes. Data engineers consider these and other factors when choosing a storage technology.
This chapter covers objective 1.1 of the Google Cloud Professional Data Engineer exam—Selecting appropriate storage technologies. In this chapter, you will learn about the following:
The business aspects of choosing a storage system
The technical aspects of choosing a storage system
The distinction between structured, semi-structured, and unstructured data models
Designing schemas for relational and NoSQL databases
By the end of this chapter, you should understand the various criteria data engineers consider when choosing a storage technology. In Chapter 2, “Building and Operationalizing Storage Systems,” we will delve into the details of Google Cloud storage services.
From Business Requirements to Storage Systems
Business requirements are the starting point for choosing a data storage system. Data engineers will use different types of storage systems for different purposes. The specific storage system you should choose is determined, in large part, by the stage of the data lifecycle for which the storage system is used.
The data lifecycle consists of four stages:
Ingest
Store
Process and analyze
Explore and visualize
Ingestion is the first stage in the data lifecycle, and it entails acquiring data and bringing data into the Google Cloud Platform (GCP). The storage stage is about persisting data to a storage system from which it can be accessed for later stages of the data lifecycle. The process and analyze stage begins with transforming data into a usable format for analysis applications. Explore and visualize is the final stage, in which insights are derived from analysis and presented in tables, charts, and other visualizations for use by others.
Ingest
The three broad ingestion modes with which data engineers typically work are as follows:
Application data
Streaming data
Batch data
Application Data
Application data is generated by applications, including mobile apps, and pushed to backend services. This data includes user-generated data, like a name and shipping address collected as part of a sales transaction. It also includes data generated by the application, such as log data. Event data, like clickstream data, is also a type of application-generated data. The volume of this kind of data depends on the number of users of the application, the types of data the application generates, and the duration of time the application is in use. This size of application data that is sent in a single operation can vary widely. A clickstream event may have less than 1KB of data, whereas an image upload could be multiple megabytes. Examples of application data include the following:
Transactions from an online retail application
Clickstream data from users reading articles on a news site
Log data from a server running computer-aided design software
User registration data from an online service
Application data can be ingested by services running in Compute Engine, Kubernetes Engine, or App Engine, for example. Application data can also be written to Stackdriver Logging or one of the managed databases, such as Cloud SQL or Cloud Datastore.
Streaming Data
Streaming data is a set of data that is typically sent in small messages that are transmitted continuously from the data source. Streaming data may be sensor data, which is data generated at regular intervals, and event data, which is data generated in response to a particular event. Examples of streaming data include the following:
Virtual machine monitoring data, such as CPU utilization rates and memory consumption data
An IoT device that sends temperature, humidity, and pressure data every minute
A customer adding an item to an online shopping cart, which then generates an event with data about the customer and the item
Streaming data often includes a timestamp indicating the time that the data was generated. This is often called the event time. Some applications will also track the time that data arrives at the beginning of the ingestion pipeline. This is known as the process time. Time-series data may require some additional processing early in the ingestion process. If a stream of data needs to be in time order for processing, then late arriving data will need to be inserted in the correct position in the stream. This can require buffering of data for a short period of time in case the data arrives out of order. Of course, there is a maximum amount of time to wait before processing data. These and other issues related to processing streaming data are discussed in Chapter 4, “Designing a Data Processing Solution.”
Streaming data is well suited for Cloud Pub/Sub ingestion, which can buffer data while applications process the data. During spikes in data ingestion in which application instances cannot keep up with the rate data is arriving, the data can be preserved in a Cloud Pub/Sub topic and processed later after application instances have a chance to catch up. Cloud Pub/Sub has global endpoints and uses GCP’s global frontend load balancer to support ingestion. The messaging service scales automatically to meet the demands of the current workload.
Batch Data
Batch data is ingested in bulk, typically in files. Examples of batch data ingestion include uploading files of data exported from one application to be processed by another. Examples of batch data include the following:
Transaction data that is collected from applications may be stored in a relational database and later exported for use by a machine learning pipeline
Archiving data in long-term storage to comply with data retention regulations
Migrating an application from on premises to the cloud by uploading files of exported data
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