Build Data Lakes and Data Warehouses on Google Cloud
Skills:
Data Warehousing85%
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.
Watch on Coursera ↗
(saves to browser)
Sign in to unlock AI tutor explanation · ⚡30
More on: Data Warehousing
View skill →Related AI Lessons
⚡
⚡
⚡
⚡
The Future of RAG: Dead, Evolving… or Becoming the Brain of AI?
Medium · Machine Learning
Smart Routing, Transfer Family Ingestion, and Voice Chat — Permission-Aware RAG v4.2
Dev.to · Yoshiki Fujiwara(藤原 善基)@AWS Community Builder
Most Companies Doing GenAI Are Really Just Doing RAG: RAGOps Explained for analysts
Medium · RAG
RAG - Sliding Window, Token Based Chunking and PDF Chunking Packages
Dev.to AI
🎓
Tutor Explanation
DeepCamp AI