Official Google Cloud Certified Professional Data Engineer Study Guide

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.

List of contents

Introduction xxiii
 
Assessment Test xxix
 
Chapter 1 Selecting Appropriate Storage Technologies 1
 
From Business Requirements to Storage Systems 2
 
Ingest 3
 
Store 5
 
Process and Analyze 6
 
Explore and Visualize 8
 
Technical Aspects of Data: Volume, Velocity, Variation, Access, and Security 8
 
Volume 8
 
Velocity 9
 
Variation in Structure 10
 
Data Access Patterns 11
 
Security Requirements 12
 
Types of Structure: Structured, Semi-Structured, and Unstructured 12
 
Structured: Transactional vs. Analytical 13
 
Semi-Structured: Fully Indexed vs. Row Key Access 13
 
Unstructured Data 15
 
Google's Storage Decision Tree 16
 
Schema Design Considerations 16
 
Relational Database Design 17
 
NoSQL Database Design 20
 
Exam Essentials 23
 
Review Questions 24
 
Chapter 2 Building and Operationalizing Storage Systems 29
 
Cloud SQL 30
 
Configuring Cloud SQL 31
 
Improving Read Performance with Read Replicas 33
 
Importing and Exporting Data 33
 
Cloud Spanner 34
 
Configuring Cloud Spanner 34
 
Replication in Cloud Spanner 35
 
Database Design Considerations 36
 
Importing and Exporting Data 36
 
Cloud Bigtable 37
 
Configuring Bigtable 37
 
Database Design Considerations 38
 
Importing and Exporting 39
 
Cloud Firestore 39
 
Cloud Firestore Data Model 40
 
Indexing and Querying 41
 
Importing and Exporting 42
 
BigQuery 42
 
BigQuery Datasets 43
 
Loading and Exporting Data 44
 
Clustering, Partitioning, and Sharding Tables 45
 
Streaming Inserts 46
 
Monitoring and Logging in BigQuery 46
 
BigQuery Cost Considerations 47
 
Tips for Optimizing BigQuery 47
 
Cloud Memorystore 48
 
Cloud Storage 50
 
Organizing Objects in a Namespace 50
 
Storage Tiers 51
 
Cloud Storage Use Cases 52
 
Data Retention and Lifecycle Management 52
 
Unmanaged Databases 53
 
Exam Essentials 54
 
Review Questions 56
 
Chapter 3 Designing Data Pipelines 61
 
Overview of Data Pipelines 62
 
Data Pipeline Stages 63
 
Types of Data Pipelines 66
 
GCP Pipeline Components 73
 
Cloud Pub/Sub 74
 
Cloud Dataflow 76
 
Cloud Dataproc 79
 
Cloud Composer 82
 
Migrating Hadoop and Spark to GCP 82
 
Exam Essentials 83
 
Review Questions 86
 
Chapter 4 Designing a Data Processing Solution 89
 
Designing Infrastructure 90
 
Choosing Infrastructure 90
 
Availability, Reliability, and Scalability of Infrastructure 93
 
Hybrid Cloud and Edge Computing 96
 
Designing for Distributed Processing 98
 
Distributed Processing: Messaging 98
 
Distributed Processing: Services 101
 
Migrating a Data Warehouse 102
 
Assessing the Current State of a Data Warehouse 102
 
Designing the Future State of a Data Warehouse 103
 
Migrating Data, Jobs, and Access Controls 104
 
Validating the Data Warehouse 105
 
Exam Essentials 105
 
Review Questions 107
 
Chapter 5 Building and Operationalizing Processing Infrastructure 111
 
Provisioning and Adjusting Processing Resources 112
 
Provisioning and Adjusting Compute Engine 113
 
Provisioning and Adjusting Kubernetes Engine 118
 
Provisioning and Adjusting Cloud Bi

About the author

DAN SULLIVAN is a software architect specializing in data architecture, machine learning, and cloud computing. Dan is a Google Cloud Certified Professional Data Engineer, Professional Architect, and Associate Cloud Engineer. Dan is the author of six books and numerous articles. He is an instructor with LinkedIn Learning and Udemy for Business.

Summary

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.

Product details

• Authors: D Sullivan, Dan Sullivan, Sullivan Dan
• Publisher: Sybex Uitgeverij
• Languages: English
• Product format: Paperback / Softback
• Released: 31.05.2020
• EAN: 9781119618430
• ISBN: 978-1-119-61843-0
• Dimensions: 190 mm x 236 mm x 20 mm
• Subjects: Education and learning > Miscellaneous; Natural sciences, medicine, IT, technology > IT, data processing; Prüfungsvorbereitung, Google, Zertifizierung, test prep, Misc (other) certifications, Sonstige Zertifizierungen