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

The following will be discussed here:

• Integrating with new data sources
• Batch and streaming
• Testing and quality control
• Data cleansing
• Building and operationalizing processing infrastructure
• Effective use of managed services (Cloud Bigtable, Cloud Spanner, Cloud SQL, BigQuery, Cloud Storage, Cloud Datastore, Cloud Memorystore)
• Provisioning resources
• Awareness of current state and how to migrate a design to a future state
• Monitoring pipelines
• Transformation
• Lifecycle management of data
• Validating a migration
• Building and operationalizing pipelines
• Building and operationalizing storage systems
• Adjusting pipelines
• Building and operationalizing data processing systems
• Storage costs and performance
• Data acquisition and import
• Migrating from on-premises to cloud (Data Transfer Service, Transfer Appliance, Cloud Networking)

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

The following will be discussed here:

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

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

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Google Cloud Big Data & Machine Learning Fundamentals course

This course is a gateway to introduce you to Google Cloud's big data and different machine learning functions. However, to successfully pass this training, you have to attain one year of experience in SQL, extract transform, data modeling, machine learning, programming in Python, and load activities. So, the objectives of the course are the following:

• Recognize the purpose of the key Big data and Machine Learning products in Google Cloud
• Create ML models using BigQuery ML, APIs, and AutoML.
• Utilize Cloud SQL & Dataproc to migrate existing MySQL, Pig, Spark, or Hive workloads to Google Cloud
• Hire BigQuery and Cloud SQL for interactive data analysis

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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.