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:

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

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The Google Professional Data Engineer certification is designed to equip the individuals with the required knowledge and skills to enable data-driven decision-making through collecting, transforming, and publishing data. To earn this certificate, the candidates will be required to pass a single test measuring their skills in leveraging, deploying, and continuously training pre-existing machine learning models. The qualifying exam also evaluates the ability of the applicants to design, build, operationalize, monitor, and secure data processing systems.

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

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

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

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:

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