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Data Engineering on Google Cloud course

It is a 4-day course that gives hands-on experience to the candidates and allows them to build data processing systems on Google Cloud. It will also show you how to design data processing systems, analyze data and build end-to-end data pipelines and machine learning. In order to get a better understanding of the course, you need to complete the big data machine learning course or get equivalent experience. This course also aids you in developing applications using a programming language such as Python and covers the following objective:

• Influencing unstructured data using ML APIs on Cloud Dataproc
• Designing and building data processing systems on the Google Cloud Platform
• Processing batch and streaming data by using autoscaling data pipelines on Cloud Dataflow
• Enable insights from streaming data
• Predicting machine models using TensorFlow and Cloud ML

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.

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

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

The following will be discussed here:

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

How to book Google Professional Data Engineer Exams

The registration for the Google Professional Data Engineer Exam follows the steps given below.

• Step4: Take the date of the exam, choose exam center and make further payment using payment method like credit/debit etc.
• Step3: Search for the exam name Google Professional Data Engineer
• Step2: Sign in or sign up to your Google Cloud Webassessor account
• Step1: Visit the Google Cloud Webassessor Website