The intensive two-day course that teaches you the secrets
of successful Logical and Physical Data Modeling.
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Also available in eLearning at firstplacelearning.com.
At this seminar you will learn the secrets of successful
logical and physical data modeling. You will gain valuable insights into
the job and responsibilities of the data modeler as well as the responsibilities
of other project contributors. This seminar helps to bridge the gap between
data modelers and database administrators (DBAs).
This seminar includes an effective mix of exercises and
presentations. Each step of the way the student learns through doing. Three
levels of data modeling are included: conceptual, logical, and physical.
The focus is on logical and physical data modeling. Students will learn
about data element analysis, standardization, naming, and normalization.
They will learn how to create a single model that supports multiple user
views. In addition, they will learn how to select and use modeling tools.
Conceptual Data Modeling also known as Entity/Relationship
(E-R) Modeling is a key method for getting a handle on the data requirements
of an organization. Effective E-R modeling results in maximum benefits
from information assets by increasing shared use and avoiding redundancy.
Data that is relevant, timely, consistent, and accessible has increased
value to the organization.
There are proven approaches, methodologies, templates,
and checklists which you will learn about that can dramatically increase
the data modeling success rate. You will learn through many effective hands
on workshops and case studies. Our trainers know how to create effective
data models and transition those models into effective database designs.
- HOW TO use data modeling to meet business and performance
requirements
- HOW TO apply data modeling to client/server projects and
data warehouse projects
- HOW TO develop logical and physical data models
- HOW TO manage and coordinate large data models
- HOW TO lead your team through the data modeling process
- HOW TO effectively translate data models into into databases
- HOW TO avoid data modeling traps, problems, and time wasters
- HOW TO improve database performance through data modeling
- HOW TO make databases easier to use through data modeling
- HOW TO select data modeling software
- HOW TO effectively communicate data models and database designs
to others
You will receive a comprehensive manual and data modeling
toolkit that provides useful checklists, examples and reference material
that will help you in your data modeling efforts. The usefulness of the course
continues long after the class sessions.
You will receive a Certificate of Completion upon
successful completion of the course.
Business Analysts
Data Architects
Data Modelers
Data Modeling Team Members
Information Technology Managers
Database Administrators
Database Developers
I. Data Modeling Concepts
- What is Data Modeling?
- Where does Data Modeling fit into Information Technology Management?
- Information Architecture
- Benefits and Objectives of Data Modeling
- Three Schema Architecture
II. Conceptual Data Modeling Review
- Preparing for Logical and Physical Modeling
- Entities
- Relationships
- Attributes
- Supertypes and Subtypes
- Derived Data
III. Defining The Data
- Discovering and Identifying Entities
- Naming Data Elements
- Standardizing Data Elements
- Re-using and Sharing Data Elements
- Business Definitions
- Using The Data Dictionary
IV. Normalizing The Data
- Normalizing Data
- Handling Derived Data
- Identifying Keys
- Relating the Entities
- Reviewing and Validating the Model
- Data Warehousing vs Transaction Processing Requirements
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V. Physical Data Modeling and Database Administration Tools
- Physical Data Modeling Tools
- DBA Tools
- Data Re-engineering Tools
- Evaluating Tools
- Managing Tools and Metadata
VI. Developing the First Cut Physical Design
- Physical Data Modeling Steps
Entity Transformation
Relationship Transformation
Normalization and De-Normalization
Defining Primary and Foreign Keys
VII. Tuning the Physical Design
- Improving Ease of Use
- Prototyping
- Ad Hoc Queries
- Transaction Modeling
VIII. Database Advanced Considerations
- Indexing
- Relational Views
- Stored Procedures
- Triggers
- Distribution of Data
- Client/Server Considerations
- Two Tier and Three Tier Architectures
- Replication of Data
- Security
- Design Tradeoffs
- Standards and Controls
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