趋近智
Data Modeling and Schema Design for Analytics
章节 1: Foundations of Analytical Architecture
Operational vs Analytical Workloads
Row-Oriented vs Columnar Storage
The Role of Denormalization
Hands-on Practical: Analyzing Query Plans
章节 2: Dimensional Modeling Core
The Grain of Data
Fact Tables Fundamentals
Dimension Tables Structure
Star Schema Architecture
Snowflake Schema Variations
Hands-on Practical: Designing a Star Schema
章节 3: Advanced Dimension Handling
Surrogate Keys vs Natural Keys
Slowly Changing Dimensions Type 1
Slowly Changing Dimensions Type 2
Date and Time Dimensions
Role-Playing Dimensions
Hands-on Practical: Implementing SCD Type 2
章节 4: Complex Fact Table Patterns
Transactional Fact Tables
Periodic Snapshot Fact Tables
Accumulating Snapshot Fact Tables
Factless Fact Tables
Hands-on Practical: Building an Accumulating Snapshot
章节 5: Physical Design and Optimization
Partitioning Strategies
Clustering and Sorting Keys
Nested Data Structures
One Big Table OBT Methodology
Hands-on Practical: Optimization Tuning
Data Modeling and Schema Design for Analytics
先修课程 Intermediate SQL knowledge
级别:
中级
Dimensional Modeling
Design efficient Star and Snowflake schemas optimized for analytical queries.
Fact and Dimension Patterns
Implement various fact table types and handle changing dimension data over time.
Storage Optimization
Apply partitioning, clustering, and denormalization strategies to improve performance.
Schema Architecture
Transition raw operational data into structured analytical models suitable for BI and reporting.
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