Course Details:
Modernizing Data Lakes and Data Warehouses with Google Cloud

The two key components of any data pipeline are data lakes and warehouses. This course highlights use-cases for each type of storage and dives into the available data lake and warehouse solutions on Google Cloud in technical detail. Also, this course describes the role of a data engineer, the benefits of a successful data pipeline to business operations, and examines why data engineering should be done in a cloud environment. This is the first course of the Data Engineering on Google Cloud series. After completing this course, enroll in the Building Batch Data Pipelines on Google Cloud course.

Skills Gained

• Differentiate between data lakes and data warehouses.
• Explore use-cases for each type of storage and the available data lake and warehouse solutions on Google Cloud.
• Discuss the role of a data engineer and the benefits of a successful data pipeline to business operations.
• Examine why data engineering should be done in a cloud environment.

Who Can Benefit

This course is intended for developers who are responsible for: Querying datasets, visualizing query results, and creating reports. Specific job roles include: Data Engineer, Data Analyst, Database Administrators, Big Data Architects

Course Outline

Introduction

• This module introduces the Data Engineering on Google Cloud source series and this Modernizing Data Lakes and Data Warehouses with Google Cloud course.

Introduction to Data Engineering

• This module discusses the role of data engineering and motivates the claim why data engineering should be done in the Cloud

Building a Data Lake

• In this module, we describe what data lake is and how to use Cloud Storage as your data lake on Google Cloud.

Building a Data Warehouse

• In this module, we talk about BigQuery as a data warehousing option on Google Cloud

Summary

• A summary of the key learning points

Course Resources

• Links to PDF versions of each module