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Course
- Cloud
Modernizing Data Lakes and Data Warehouses with Google Cloud
The two key components of any data pipeline are data lakes and warehouses.
What you'll learn
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.
Table of contents
- Module introduction | 1m 52s
- The role of a data engineer | 4m 18s
- Data engineering challenges | 5m 29s
- Introduction to BigQuery | 2m 24s
- Data lakes and data warehouses | 4m 44s
- Transactional databases versus data warehouses | 4m 30s
- Partner effectively with other data teams | 5m 16s
- Manage data access and governance | 1m 42s
- Demo: Finding PII in your dataset with the DLP API | 1m 57s
- Build production-ready pipelines | 2m 1s
- Google Cloud customer case study | 1m
- Recap | 1m 2s
- Lab Intro: Using BigQuery to do Analysis | 13s
- Getting Started with GCP and Qwiklabs | 3m 48s
- Lab: Using BigQuery to do Analysis | 10s
