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

The following will be discussed here:

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

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The benefit of obtaining the Google Professional Data Engineer Exam Certification

A Professional Data Engineer enables data-driven decision making by collecting, transforming, and publishing data. A data engineer should be able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. A data engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models.

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

Requirements

The certification does not have any official prerequisites. However, it is advised to have at least three years of industry experience with one or more years of expertise in designing and managing different solutions with the use of Google Cloud Platform. It is also required to review the topics of the qualifying exam before sitting for it.

Understanding functional and technical aspects of Google Professional Data Engineer Exam Designing data processing systems

The following will be discussed here:

• Use of distributed systems
• Hybrid cloud and edge computing
• Data modeling
• Designing data processing systems
• Data publishing and visualization (e.g., BigQuery)
• At least once, in-order, and exactly once, etc., event processing
• Batch and streaming data (e.g., Cloud Dataflow, Cloud Dataproc, Apache Beam, Apache Spark and Hadoop ecosystem, Cloud Pub/Sub, Apache Kafka)
• Distributed systems
• Choice of infrastructure
• Schema design
• Designing data pipelines
• Selecting the appropriate storage technologies
• Architecture options (e.g., message brokers, message queues, middleware, service-oriented architecture, serverless functions)
• Job automation and orchestration (e.g., Cloud Composer)
• Mapping storage systems to business requirements
• Tradeoffs involving latency, throughput, transactions
• Online (interactive) vs. batch predictions
• System availability and fault tolerance
• Capacity planning

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