Official Google Cloud Certified Professional Data Engineer Study Guide

Dan Sullivan

• Publisher: Sybex
• Year: 2020
• Print ISBN: 9781119618430
• eBook ISBN: 9781119618454
• Edition: 1
• Topic: Computer Science
• Subtopic: Cloud Computing
• Index: Computer Science

The proven Study Guide that prepares you for this new Google Cloud exam

The Google Cloud Certified Professional Data Engineer Study Guide, provides everything you need to prepare for this important exam and master the skills necessary to land that coveted Google Cloud Professional Data Engineer certification. Beginning with a pre-book assessment quiz to evaluate what you know before you begin, each chapter features exam objectives and review questions, plus the online learning environment includes additional complete practice tests.

Written by Dan Sullivan, a popular and experienced online course author for machine learning, big data, and Cloud topics, Google Cloud Certified Professional Data Engineer Study Guide is your ace in the hole for deploying and managing analytics and machine learning applications.

• Build and operationalize storage systems, pipelines, and compute infrastructure
• Understand machine learning models and learn how to select pre-built models
• Monitor and troubleshoot machine learning models
• Design analytics and machine learning applications that are secure, scalable, and highly available.

This exam guide is designed to help you develop an in depth understanding of data engineering and machine learning on Google Cloud Platform.

Chapter 1
Selecting Appropriate Storage Technologies

Google Cloud Professional Data Engineer Exam objectives covered in this chapter include the following:

1. Designing data processing systems
   • ✔ 1.1 Selecting the appropriate storage technologies
     • Mapping storage systems to business requirements
     • Data modeling
     • Tradeoffs involving latency, throughput, transactions
     • Distributed systems
     • Schema design

Data engineers choose how to store data for many different situations. Sometimes data is written to a temporary staging area, where it stays only seconds or less before it is read by an application and deleted. In other cases, data engineers arrange long-term archival storage for data that needs to be retained for years. Data engineers are increasingly called on to work with data that streams into storage constantly and in high volumes. Internet of Things (IoT) devices are an example of streaming data.

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 m...

Table of contents

• Cover
• Title Page
• Copyright
• Dedication
• Acknowledgments
• About the Author
• About the Technical Editor
• Introduction
• Assessment Test
• Answers to Assessment Test
• Chapter 1 Selecting Appropriate Storage Technologies
• Chapter 2 Building and Operationalizing Storage Systems
• Chapter 3 Designing Data Pipelines
• Chapter 4 Designing a Data Processing Solution
• Chapter 5 Building and Operationalizing Processing Infrastructure
• Chapter 6 Designing for Security and Compliance
• Chapter 7 Designing Databases for Reliability, Scalability, and Availability
• Chapter 8 Understanding Data Operations for Flexibility and Portability
• Chapter 9 Deploying Machine Learning Pipelines
• Chapter 10 Choosing Training and Serving Infrastructure
• Chapter 11 Measuring, Monitoring, and Troubleshooting Machine Learning Models
• Chapter 12 Leveraging Prebuilt Models as a Service
• Appendix Answers to Review Questions
• Index
• End User License Agreement