Dan Sullivan

Official Google Cloud Certified Professional Data Engineer Study Guide (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: John Wiley & Sons
• Erscheinungstermin: 18. Mai 2020
• Englisch
• ISBN-13: 9781119618447
• Artikelnr.: 59540833

Autorenporträt

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.

Inhaltsangabe

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