The third course in this course series is Achieving Advanced Insights with BigQuery. Here we will build on your growing knowledge of SQL as we dive into advanced functions and how to break apart a complex query into manageable steps. We will cover the internal architecture of BigQuery (column-based sharded storage) and advanced SQL topics like nested and repeated fields through the use of Arrays and Structs. Lastly we will dive into optimizing your queries for performance and how you can secure your data through authorized views. After completing this course, enroll in the Applying Machine Learning to your Data with Google Cloud course.
Overview
Syllabus
- Introduction
- Course Introduction
- Advanced Functions and Clauses
- Advanced functions (Statistical, analytic, user-defined)
- Analytic functions, WITH clause, and RANK()
- BigQuery User-Defined Functions (UDFs)
- Sub-query and CTE design
- Lab Intro: Deriving Insights from Advanced SQL Functions
- Deriving Insights from Advanced SQL Functions v1.5
- Quiz: Advanced Functions
- Schema Design and Nested Data Structures
- BigQuery versus traditional relational data architecture
- Denormalized, column-based storage
- Table sharding
- Introducing nested and repeated fields
- Introducing arrays and structs
- Flattening arrays: Legacy versus Standard
- Module summary
- Working with JSON, Arrays, and Structs in BigQuery v1.5
- Quiz: BigQuery Architecture
- Optimizing for Performance
- Avoid BigQuery performance pitfalls
- Prevent hotspots in your data
- Diagnose performance issues
- Lab Intro: Optimizing BigQuery for Cost and Performance
- Optimizing BigQuery for Cost and Performance v1.5
- Quiz: Performance Optimization
- Advanced Insights with Vertex AI Workbench
- A primer on Notebooks in the Cloud
- BigQuery in JupyterLab on Vertex AI
- Data Access
- Data Access
- Quiz: Data Access
- Course Summary
- Course summary
- Course Resources
- Course Resources
- Your Next Steps
- Course Badge
