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For more information, please refer to Whitepaper : Model Behavior: An Introduction to Data Models .

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Model behavior: An Introduction to data models

Data models:

  • Organizes data elements.
  • Standardizes relation of data elements to each other.
  • Standardizes relation of data elements to properties of real-world entities.

Database models:

  • A type of data model.
  • Determines a database’s logical structure.
  • Determines how data can be stored, organized, and manipulated.

A normalized relational database and normalized star schema are two of the most common database models, each of which as its advantages.

Normalized relational database

  • Uses tables to make databases independent of software applications.

Database normalization:

  • Process of structuring relational database.
  • Reduces data redundancy and improves data integrity.
  • Organizes columns or attributes in tables.
  • Enforces various data integrity constraints.
  • Must meet requirements for previous normalization level before next level can be achieved.

Unnormalized data:

  • May or may not have primary key.
  • Table does not have duplicate records.

First normal form (1NF):

  • Requires primary key.
  • Requires all cells in tables to have single values.
  • Move attributes that could have multiple values to separate table.
  • Enforces various data integrity constraints.
  • Create primary key to connect new table to old table.

Second normal form (2NF)

  • 1NF plus removing table’s partial dependencies.
  • All attributes must depend on entire primary key.

Third normal form (3NF):

  • 2NF plus removing all transitive dependencies.
  • Attributes may only depend on primary key.

Star schema:

  • Post-relational data model.
  • More generalized data model than relational data model.
  • Most common data model in data warehouses.
  • Unnormalized data.
  • Consists of at least one fact table that references any number of dimension tables.

Fact table:

  • Records measurements for specific events.
  • Usually consists of numeric values and foreign key.
  • Foreign key links to dimensional table containing descriptive information for numeric values.
  • Records measurements at low level of detail or granularity.
  • Results in accumulation of many records over time.
  • Three types: Transaction, snapshot, and accumulating snapshot fact tables.

Dimension table:

  • Has smaller number of records compared to fact table
  • Number of attributes in their records can be very large.
  • Defines many types of dimensions:
    Time: (most common) Describe time at which events are recorded in fact table.
    Range: Describe range of measurable quantities to simplify reporting.
    Other: Tables for employees, geography, and products.

Comparison:

Advantages of normalized relational database versus star schema:

  • Strictly reinforces data integrity to prevent anomalies like one-off inserts and updates.
  • Greater flexibility in performing analytical tasks if they follow database model’s business logic.
  • More easily supports many-to-many relationships between business entities.

Benefits of star schema versus normalized relational database:

Simpler reporting logic, especially as-of and period-over-period reporting.

Simpler join-logic:

  • Improves performance on read-only operations like reporting and queries.
  • Improves performance of aggregration operations.
  • Efficiently build proprietary cubs for online analytical processing (OLAP) systems.

Topics : Data Governance,Data Modeling,

Products : ER/Studio Data Architect,ER/Studio Enterprise Team Edition,

Model Behavior: An Introduction to Data Models

A data model organizes data elements and standardizes their relation to each other and the properties of real-world entities. A database model is a type of data model that determines a database’s logical structure how data can be stored, organized and manipulated in that database. A normalized relational database and unnormalized star schema are two of the most common database models, each of which has its advantages.

ER/Studio Data Architect enables you to efficiently catalog your current data assets and sources across different platforms and track end-to-end data lineage. Simplify your data architecture with a common language leveraging consistent naming standards and data definitions. Easily specify the sensitive data objects that need heightened protection, to withstand audit scrutiny. Learn more →.

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