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.
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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.
This course is part of the Google Cloud Data Engineer Learning Path Professional Certificate.
What you'll learn
- 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.
Prerequisites:
To benefit from this course, participants should have completed “Google Cloud Big Data and Machine Learning Fundamentals” or have equivalent experience. Participant should also have: • Basic proficiency with a common query language such as SQL. • Experience with data modeling and ETL (extract, transform, load) activities. • Experience with developing applications using a common programming language such as Python. • Familiarity with machine learning and/or statistics
Syllabus
- 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.
