Google Cloud Professional Data Engineer Cert Prep: 1 Designing Data Processing Systems

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Noah Gift

1:04:00

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01 - Introduction

01 - Google professional data engineer course overview.mp4

03:50

02 - Onboard to GCP.mp4

08:05

02 - 1. Storage Technology Selection

01 - Open-source vs. Google Cloud managed services.mp4

02:30

02 - Pro vs. con of open-source data engineering tools.mp4

05:17

03 - Google Cloud analytics services.mp4

02:21

03 - 2. Data Pipeline Design

01 - Data engineering pipelines.mp4

03:06

02 - Google Cloud storage strategy.mp4

03:48

04 - 3. Data Warehousing and Processing Migration

01 - Overview GCP storage.mp4

02:05

02 - Optimize for GCP database solutions.mp4

03:46

03 - Prompt engineering for BigQuery.mp4

09:20

04 - Using Google BigQuery with Google Colab.mp4

05:32

05 - Exploring data with Google BigQuery.mp4

12:36

05 - Conclusion

01 - Next steps.mp4

01:44

Description

Professional Data Engineers enable data-driven decision making by collecting, transforming, and publishing data. 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. A Data Engineer should also be able to leverage, deploy, and continuously train pre-existing machine learning models. In this course, Noah Gift prepares you for the first part of the exam, focusing on designing data processing systems. He covers topics on data warehousing and processions migration, storage technology selection, data pipeline design, and data processing solution design.

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Data Science

Data Analysis

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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LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.