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

Exam Details

The Google Professional Data Engineer certification exam has the duration of 2 hours. The qualifying test is made up of multiple-select and multiple-choice questions. The exam is available either in Japanese or English. To register for it, you are required to go through the official webpage and pay the fee of $200 plus applicable taxes. While completing the registration process, the potential individuals can choose the preferred method of exam delivery. It can be taken in person at the nearest testing center or online from a remote location.

This course is normally taken by data scientists, data analysts, and business analysts who are in the field of Google Сloud. It is a good way to prepare for your final exam because it teaches you all the details including 7 modules that cover all the Professional Data Engineer exam objectives:

• Data Analytics on the Cloud
• Data Processing Architectures
• Machine Learning
• Scaling Data Analytics
• Introducing Google Cloud Platform ‘
• Additional Resources
• Compute and Storage Fundamentals

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The following will be discussed here:

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

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

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