About this course

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.

At a Glance:
Institution: GoogleCloud
Subject: Computer Science
Level: Introductory
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
Language: English
Video Transcript: English
Associated programs:
Professional Certificate in Google Cloud Data Engineer Learning Path
Associated skills:Warehousing, Google Cloud, Data Warehousing, Database Administration, Data Lakes, Data Engineering, Data Analysis, Business Operations, Data Pipeline, Big Data