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.
- 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.
- This module introduces the Data Engineering on Google Cloud source series and this Modernizing Data Lakes and Data Warehouses with Google Cloud course.
2. 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.
3. 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.
4. Building a Data Warehouse
- In this module, we talk about BigQuery as a data warehousing option on Google Cloud.
- A summary of the key learning points.
5. Course Resources
- Links to PDF versions of each module.
- Lectures 0
- Quizzes 0
- Duration 1 week
- Skill level All levels
- Language English
- Students 0
- Assessments Yes