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.

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.

Conclusion

Don't wait and enroll in these training courses offered by the official vendor that will help you ace the Professional Data Engineer exam with a good score. Once you take this exam and earn the prestigious Professional Data Engineer certification, you will get a chance to obtain a high-paying job and an amazing opportunity to work with the experts. Don't waste your time on other tasks and start preparing for this exam today. The more you practice the more you will get closer to success as a data analyst or data engineer. Moreover, it will polish your skills throughout and allow you to efficiently in well-reputed companies.

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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 Processing Infrastructure: The considerations for this subject area include provisioning resources, adjusting pipeline, monitoring pipeline, and testing & quality control.
• 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;

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

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