Understanding functional and technical aspects of Google Professional Data Engineer Exam Ensuring solution quality

The following will be discussed here:

• Assessing, troubleshooting, and improving data representations and data processing infrastructure
• Choosing between ACID, idempotent, eventually consistent requirements
• Planning, executing, and stress testing data recovery (fault tolerance, rerunning failed jobs, performing retrospective re-analysis)
• Data security (encryption, key management)
• Legal compliance (e.g., Health Insurance Portability and Accountability Act (HIPAA), Children's Online Privacy Protection Act (COPPA), FedRAMP, General Data Protection Regulation (GDPR))
• Designing for security and compliance
• Ensuring scalability and efficiency
• Verification and monitoring
• Data staging, cataloging, and discovery
• Ensuring flexibility and portability
• Mapping to current and future business requirements
• Building and running test suites
• Resizing and autoscaling resources
• Designing for data and application portability (e.g., multi-cloud, data residency requirements)
• Pipeline monitoring (e.g., Stackdriver)
• Ensuring privacy (e.g., Data Loss Prevention API)
• Ensuring reliability and fidelity
• Identity and access management (e.g.,Cloud IAM)
• Performing data preparation and quality control (e.g., Cloud Dataprep)

Reference: https://cloud.google.com/certification/data-engineer

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Who should take the Google Professional Data Engineer exam

Individuals should pursue the exam if they want to demonstrate their expertise and ability to design and develop Data Engineering. Following professional get benefited from Google Professional Data Engineer Certification

• Data architects
• Data engineers
• Data scientists
• Business analysts
• Data analysts
• Developers responsible for managing big data transformation initiatives

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