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Improve Your Data Modeling Skills

Data professionals should know that improving their data modeling skills increases productivity
and efficiency. Certifications can demonstrate these skills, which also improves your
marketability. Learning the skills discussed here will help achieve the success you want by
allowing you to become a sought-after data modeler. Data modeling skills include a basic
understanding of how to create and read such a model. Fundamental data design techniques are
also essential skills for data modelers.

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.


Improve Your Data Modeling Skills

Data professionals should know that improving their data modeling skills
increases productivity and efficiency. Certifications can demonstrate these
skills, which also improves your marketability.


Essential data modeling skills:

  • basic understanding of how to create and read data models
  • fundamental data design techniques

Data Modeling

  • process of analyzing data-oriented structures
  • includes variety of specific model types
  • types range from models for physical data to models for high-level
    o similar to class modeling for object-oriented (OO) design
  • data modelers versus OO developers:
    . model entity types versus classes
    . assign attributes to entity types versus attributes and
    operations to classes
    . associations between entities versus between classes in OO
    design: similar

Entity types

o understanding entity types is fundamental skill for data models
o entity types represent:

  • collection of similar objects (such as people, places, and things)
  • non-physical concepts (such as events)
  • example: in order entry database: Customer, Order, and Item are
    common entity types
    o entity types only represent data whereas classes also describe
    object’s behavior


o entity types have at least one attribute

o example: attributes for entity type Customer typically include attributes
First Name and Last Name

o developers typically implement attributes as columns in database tables

  • achieving optimum level of detail is often challenging

    o expressing single attributes with multiple columns:
  • can provide greater control over data
  • incurs development and maintenance costs

    o example: phone number in North America
  • has three components: Area Code, Prefix, and Line Number
  • rarely need to assign each component to separate columns

Naming Conventions

o naming conventions for data modeling:

  • typically maintained by enterprise administrators
  • essential for making code easy to understand and modify
  • physical and logical data models typically have different naming
    conventions since they have different purposes

    o example:
  • for logical data models: give greater priority to human readability
  • for physical models: focus more on technical considerations


o relationships between entities:

  • key requirement for developing data modeling skills
  • conceptually identical to associations between objects in OO
  • example: order entry system:

    . Customers place Orders, so placement is typical relationship
    between customers and orders

    . Customers live at Address, and Zip Code is part of Address

    o naming relationships often becomes unnecessary when specifying
    entities’ role in relationships with sufficient clarity

Key Assignment

o data modeling uses two basic strategies to assign keys to tables:

  • assigning natural key:

    . usually best option when table has at least one attribute that is
    unique to table’s business concept
  • create surrogate key:

    . data modelers need to add new column for tables without such

    ~ no business meaning

    ~ merely serves to identify entity type

    . example:

    ~ addresses do not have obvious natural key because needs entire
    address to identify it

    ~ data modelers often identify addresses with surrogate key called
    something like Address Identifier


o process of organizing data within data models

o make entity types of data models more cohesive

o generally involves reducing data redundancy

  • highly beneficial for application development
  • storing objects in relational databases becomes much easier when
    information about those objects is maintained in only one place

    o first three levels of normalization are most common

    o higher levels are possible

    o progressive hierarchy: next level meets all requirements of
    previous level

    o example:
  • entity type in first normal form (1NF):

    . does not contain repeating data groups
  • entity type in second normal form (2NF)

    . in 1NF

    . its non-key attributes fully dependent on its primary key
  • entity type is in third normal form (3NF):

    . in 2NF

    . its attributes directly dependent on primary key

    o incurs performance cost
  • denormalization also important skill for data modelers
  • data models often bear little resemblance to their normalized

In addition to proper training, the key to improving data modeling skills
is practice.

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