Google Cloud – Professional Data Engineer Certification Exam Notes

Google Cloud Data Engineer Study Guide

Table of Contents

• Section 1: Designing Data Processing Systems
• Section 2: Ingesting and Processing the Data
• Section 3: Storing the Data
• Section 4: Preparing and Using Data for Analysis
• Section 5: Maintaining and Automating Data Workloads

Section 1: Designing Data Processing Systems (~22%)

1.1 Designing for Security and Compliance

• Identity and Access Management (e.g., Cloud IAM and organization policies)
• Data security (encryption and key management)
• Privacy (e.g., personally identifiable information, and Cloud Data Loss Prevention API)
• Regional considerations (data sovereignty) for data access and storage
• Legal and regulatory compliance

1.2 Designing for Reliability and Fidelity

• Preparing and cleaning data (e.g., Dataprep, Dataflow, and Cloud Data Fusion)
• Monitoring and orchestration of data pipelines
• Disaster recovery and fault tolerance
• Making decisions related to ACID compliance and availability
• Data validation

1.3 Designing for Flexibility and Portability

• Mapping current and future business requirements to the architecture
• Designing for data and application portability (multi-cloud, data residency)
• Data staging, cataloging, and discovery (data governance)

1.4 Designing Data Migrations

• Analyzing current stakeholder needs and planning for desired state
• Planning migration to Google Cloud (e.g., BigQuery, Datastream)
• Designing migration validation strategy
• Designing dataset and table architecture for proper governance