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11.1 Introduction to Database Design and Modeling
Overview of database design principles
Importance of effective database modeling
Key concepts: Entities, attributes, relationships, and tables
11.2 Entity-Relationship (ER) Modeling
Introduction to ER modeling
Entities, attributes, and relationships in ER diagrams
Cardinality and participation constraints
Mapping ER diagrams to relational schemas
11.3 Normalization and Denormalization
Understanding normalization and denormalization
Normal forms: First normal form (1NF) to Boyce-Codd normal form (BCNF)
Benefits and trade-offs of normalization and denormalization
11.4 Relational Schema Design
Conceptual, logical, and physical database design
Translating ER diagrams into relational schemas
Primary keys, foreign keys, and referential integrity constraints
11.5 Data Modeling Best Practices
Best practices for designing effective data models
Identifying and resolving data modeling challenges
Iterative and incremental data modeling process
11.6 Schema Refinement and Optimization
Refining and optimizing database schemas
Indexing strategies for improving query performance
Partitioning and clustering for efficient data storage and retrieval
11.7 Temporal and Spatial Data Modeling
Modeling temporal data: Effective dating, event tracking
Spatial data modeling: Geospatial data types, spatial indexing
Use cases and applications of temporal and spatial data modeling
11.8 Data Warehousing and Dimensional Modeling
Introduction to data warehousing concepts
Dimensional modeling techniques: Star schema, snowflake schema
Designing data marts and OLAP cubes
11.9 Modeling Complex Data Structures
Handling complex data structures in database modeling
Arrays, nested tables, and other composite data types
Techniques for modeling hierarchical data
11.10 Modeling for NoSQL Databases
Data modeling considerations for NoSQL databases
Document-oriented, key-value, columnar, and graph data modeling
Schema flexibility and dynamic schema evolution in NoSQL databases
11.11 Modeling for Big Data and Analytics
Designing data models for big data and analytics
Handling unstructured and semi-structured data
Data modeling techniques for machine learning and predictive analytics
11.12 Data Governance and Documentation
Establishing data governance policies and procedures
Documenting database designs and data dictionaries
Ensuring data quality and consistency through data governance practices
11.13 Data modeling best practices
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