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Basic Concepts
Data Subjects / Entities: Something that “exists”, like student, grade, etc. Not equal to database tables. Database tables are sometimes artificial, duplicative(aggregates)
- Strong Entity: has a primary key.
- Weak Entity: has the partial key which acts as a discriminator between the entities of a weak entity set.
- Data Attributes of the Data Subjects: field / database column, like ID, name, etc.
Relationship between Data Subjects: like instructor teaches a class, class is taught by an instructor.
- Gerund: A relationship that also exhibits characteristics of an entity, and can have attributes attached to it.
- Business Rules applied to our data: Cardinality（一个列中不同值的个数）, mandatory or optional relationships, permissible attribute values (like NULL), data change dynamics.
Modeling
Systems Modeling
Data Modeling
- Classic ER (Entity–Relationship)
- Post Classic ER
System Modeling
- Semantic
- UML: Unified Modeling Language
Database Design vs Data Modeling
Database Design
- Specific DBMS(Database Management System) model (e.g. relational)
- Goes below schema to physical storage
- Implementation/product specific restrictions from the very beginning
Data Modeling
- Conceptual / Semantic level
- Unconstraint by RDBMS(Relational Database Management System) or other implementation rules
- Closer to real world
Data Modeling Life Cycle
Conceptual Modeling
Logical Modeling
Physical Modeling
Data Modeling Methodologies
Transactional:
- Conceptual level: mirror real world
Logical level:
- Relational: data normalization with deliberate denormalization
- Non Relational: NoSQL, OODBMS(Object-Oriented Database Management System) constructs, etc.
- Physical level: blocks/tracks, MPP(Massively Parallel Processing) distribution, etc.
Analytical (DW):
- Conceptual level: dimensional
Logical level:
- Relational: fact and dimension tables
- Non Relational: cubes, culumnardatabases, etc.
- Physical level: blocks/tracks, MPP(Massively Parallel Processing) distribution, AWS buckets, HDFS name nodes and data nodes, etc.
Classic ER Notation / Chen Notation
Multi Valued Attribute (MVA): like one person can have multiple email address
Crow’s Foot Notation
More closed aligned with logical modeling
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