Google Cloud Professional Data Engineer Cert Prep: 2 Building and Operationalizing Data Processing Systems

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Noah Gift

50:59

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01 - Introduction

01 - Course overview.mp4

02:02

02 - 1. Storage System Implementation

01 - Demo Google Cloud Shell.mp4

04:16

02 - Demo Google Cloud Editor.mp4

04:41

03 - Demo Google CLI SDK.mp4

06:14

04 - Demo Google gcloud CLI tool.mp4

04:50

05 - Storage comparison.mp4

03:51

03 - 2. Pipeline Building and Operationalization

01 - Jack and the Beanstalk as a data pipeline.mp4

03:07

02 - Compare compute offerings.mp4

02:24

03 - Demo Compute volatility on GCP.mp4

03:21

04 - 3. Processing Infrastructure Implementation

01 - The challenges of big data.mp4

01:57

02 - Demo Extending GCP Cloud Functions.mp4

10:06

03 - Data pipeline triggers.mp4

02:35

05 - Conclusion

01 - Next steps.mp4

01:35

Description

Earning the Google Cloud Professional Data Engineer certification confirms that you’re able to design, build, operationalize, secure, and monitor data processing systems with a particular emphasis on security and compliance; scalability and efficiency; reliability and fidelity; and flexibility and portability. In this course, Noah Gift prepares you for the section of the exam that focuses on building and operationalizing data processing systems. Noah covers the topics tested on the exam: storage system implementation, pipeline building and operationalization, and processing infrastructure implementation.

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Data Science

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Google Cloud

Noah Gift

Instructor's Courses

Author of Practical MLOps, Enterprise MLOps, Developing on AWS with C#, Pragmatic AI, and Python for DevOps. Certified on Multiple MLOps certifications including Google-Professional Machine Learning Engineer and AWS Certified Machine Learning - Specialty. Adjunct Professor at Duke MIDS & Northwestern Graduate Data Science & AI. Held business roles including CTO, general manager, consulting CTO, and cloud architect. Consults with start-ups and other companies on machine learning and cloud architecture. AWS ML Hero, Python Software Foundation Fellow, AWS Subject Matter on Machine Learning, AWS Certified Solutions Architect, AWS Certified Machine Learning Specialist, AWS Certified Big Data Specialist, Google Certified Professional Architect, and AWS Academy Accredited Instructor. Published books, videos, and courses on cloud machine learning, DevOps, Python, Data Science, Big Data and AI. ★ Specialties ★ ° Cloud-native Machine Learning and AI ° Directly teaching cutting edge skills to students that lead to jobs ° Creating world-class content in all forms ° Building Companies ° Shipping new Products ° Leading and growing engineering teams ° Production Machine Learning, Deep Learning, Big Data, and AI ° Serverless Data Engineering ° Advising Early Stage Startups/Consulting CTO services ° Distributed Systems and Scalability

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