Member since 2025
Building Batch Data Pipelines on Google Cloud
Earned Sep 15, 2025 EDT
Modernizing Data Lakes and Data Warehouses with Google Cloud
Earned Sep 10, 2025 EDT
Introduction to Data Engineering on Google Cloud
Earned Sep 3, 2025 EDT
Data pipelines typically fall under one of the Extract and Load (EL), Extract, Load and Transform (ELT) or Extract, Transform and Load (ETL) paradigms. This course describes which paradigm should be used and when for batch data. Furthermore, this course covers several technologies on Google Cloud for data transformation including BigQuery, executing Spark on Dataproc, pipeline graphs in Cloud Data Fusion and serverless data processing with Dataflow. Learners get hands-on experience building data pipeline components on Google Cloud using Qwiklabs.
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.
In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.