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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.
Autorentext
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.
Klappentext
Includes interactive online learning environment and study tools:
Searchable key term glossary Understand the Google Cloud Platform and prepare for the new Professional Data Engineer exam The Official Google Cloud Certified Professional Data Engineer is your one-stop resource for complete coverage of the new Google Cloud Professional Data Engineer exam. This Sybex Study Guide covers 100% of the Google Cloud Professional Data Engineer exam objectives. You'll prepare for the exam smarter and faster with Sybex, thanks to a pre-book assessment quiz to test what you already know, practice tests that check exam readiness, Exam Essentials review sections, and challenging chapter review questions. Reinforce what you have learned with the exclusive Sybex online learning environment and test bank, assessable across multiple devices. Get prepared for the Google Cloud Professional Data Engineer exam with Sybex. Coverage of 100% of all exam objectives in this Study Guide means you'll be ready for:
Pre-Built Machine Learning Models Interactive learning environment Take your exam prep to the next level with Sybex's superior interactive online study tools. To access our learning environment, simply visit www.wiley.com/go/sybextestprep, register your book to receive your unique PIN, and instantly gain access to:
Zusammenfassung
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.
Inhalt
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
…