Google Certified Professional Data Engineer : Professional-Data-Engineer

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.
• Step2: Sign in or sign up to your Google Cloud Webassessor account
• Step3: Search for the exam name Google Professional Data Engineer
• Step1: Visit the Google Cloud Webassessor Website

Build & Operationalize Data Processing Systems

• Build & Operationalize Storage Systems: This part will require the students’ skills and competence in the effective usage of managed services, including Cloud Spanner, CLoug Bigtable, BigQuery, Cloud SQL, Cloud Memorystore, Cloud Datastore, and Cloud Storage. It also covers their skills in managing the data lifecycle and storage performance and costs;
• Build & Operationalize Pipeline: This module requires that the learners demonstrate competence in data cleansing, transformation, batch & streaming, data import & acquisition, as well as integration with the new data sources;
• Build & Operationalize Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.

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Career Opportunities

The certified individuals can explore a variety of job opportunities. Some of the positions that they can take up include a Software Engineer, a Cloud Architect, a Data Engineer, a Sales Engineer, a Data Scientist, a Cloud Developer, and a Kubernetes Architect, among others. The salary outlook for these job roles is an average of $128,500 per annum.

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

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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:

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

Google Professional-Data-Engineer Exam Syllabus Topics: