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Course Outline
Introduction
- Definition of Analytic Functions
- Advantages and practical use cases
- Survey of common Analytic Functions
Core Analytic Functions
- ROW_NUMBER(), RANK(), DENSE_RANK()
- Mastering PARTITION BY and ORDER BY clauses
- Illustrative examples and scenarios
Statistical Analytic Functions
- SUM(), AVG(), MIN(), MAX()
- LEAD() and LAG()
- Application scenarios
Windowing Clause
- Exploring the WINDOWING clause
- Clarifying UNBOUNDED, CURRENT ROW, and N PRECEDING/FOLLOWING
- Practical implementations
Advanced Analytic Functions
- FIRST_VALUE() and LAST_VALUE()
- PERCENTILE_CONT() and PERCENTILE_DISC()
- Comparative analysis and use cases
Complex Queries Using Analytic Functions
- Integrating Analytic Functions with GROUP BY
- Implementing nested Analytic Functions
- Real-world case studies
Optimizing Analytic Functions
- Efficient application of Analytic Functions on large datasets
- Evaluating query performance
- Effective indexing strategies
Troubleshooting and Best Practices
- Diagnosing and resolving common issues
- Guidelines for writing efficient queries
- Advice for maintaining and updating Analytic Function queries
Summary and Next Steps
Requirements
- Fundamental knowledge of SQL
- Understanding of relational databases
- Intermediate programming experience, ideally with SQL
Target Audience
- Database administrators
- SQL developers
- Data analysts
21 Hours
Testimonials (2)
Doing Exercise
Joe Pang - Lands Department, Hong Kong
Course - QGIS for Geographic Information System
Hands-on examples allowed us to get an actual feel for how the program works. Good explanations and integration of theoretical concepts and how they relate to practical applications.
