OLTP (RDBMS) Data Modeling Training

Online Data Modeling Training on

OLTP, Data Warehouse, Datamart, Dimensional and Snow Flake Data Modeling and Normalization. end to end process

Course# 2 – Online Advanced Data Modeling Training

Course Description:

This online training course explains in detail about database, data warehouse, data modeling concepts, data modeling types and how these are used in OLTP environments and Data warehouse / Datamart Environments using Erwin (OR) Power Designer (OR) Oracle SQL Data Modeler.

To get more information about this training program, send an email to antony@antonystraining.com 0r call us @ 91-9080157239.

Sample Data Models:

Banking Data Model, Mortgage Data Model, Clinic Data Model, Training Data Model, Staffing Data Model

Normalization Examples (upto 3rd Normal Form) etc.

Student, Clinic, Order, Auto Rental Agreement, Contract Work

Course Information:

Prerequisites:

Internet connection
Lap Top or Desk Top

Tools:

Erwin OR Power Designer OR Oracle SQL Data Modeler

Training Certificates:

Will be provided.

Course Syllabus

Part 1 – Career Path of a Data Modeler

What is a Data Modeling?
Explanation of Data Modeler duties in brief
Certifications in Data Modeling
Career Path of a Data Modeler
Salary of a Data Modeler

Part 2 – Data Modeling Concepts

Who is a data modeler?
What are the other alternative titles for a Data Modeler?
What are the duties and responsibilities of a Data Modeler?
What is the difference between duty and responsibility?
What is a Data Model?
Who needs Data Modeling?
Different Data Modeling Tools
IDEF1X and IE Methodology

Part 3 – Data Modeling Types

Logical Data Model
Physical Data Model
Dimensional Data Model
Conceptual Data Model
Enterprise Data Model
Data Modeling Development Life Cycle

Part 4– Data Model Standards

Naming standards of objects
Abbreviating column names
Consistency in Column Names
Why it is important

Part 5 – Database Explanation from Data Modeling Perspective

Main object: Table, Column, Datatype
Constraints: NULL, NOT NULL, Primary Key, Unique, Check, Default Value
Other objects: Database, Schema, Tablespace, Segment, Extent, Privileges, Index, View, Synonym
DDL Statements: CREATE, ALTER, DROP
DML Statements: INSERT, UPDATE, DELETE

Part 6– How to create a logical Data Model

Entity, Attribute, Primary Key, Alternate Key, Inversion Key Entry, Rule, Relationship, Definition, Index, Unique Index

Part 7– Relationships

Identifying, Non-Identifying, Many to Many
Cardinality
One to One Relationship
One to many relationship
Many to many relationship
Whether Zero option is required or not
Resolving Many to Many Relationship
Self-Referential Integrity Relationship
Normalization process – 1NF, 2NF, 3NF
Supertypes and Subtypes

Part 8 – How to create a Physical data model:

Table, Column
Primary Key Constraint, Unique Constraint Check Constraint, Foreign Key Constraint, Comment
Default Value
Unique Index, Non-Unique Index,
Difference between a logical data model and Physical Data Model

Part 9 – Physical Data Model, Database & Scripts:

What is Forward Engineering?
How to generate scripts from a data model and share it with DBA?
What is Reverse Engineering?
How to create a data model from a database?
How to create a data model from a script?
How to compare data models?
How to compare database and a data model?
What is subject area?
Why do we need so many subject areas?
How to implement Physical data model in a database?
How to generate SQL Code?
How to implement it in Database?

Part 10: Concepts: Dimensional Data Modeling, Data Warehouse and Data Mart

What is a Lookup?
How to maintain data in Lookups?
What is a Data Warehouse?
What is a Data Mart?
What is a Dimension?
What is Snow Flake Modeling?
What is Star Schema Modeling?
What is Slowly Changing Dimensions?
What is Dimensional Data Modeling?
How to create a data model for Data Warehouse and Data Mart?
What is ETL?
Things to learn for mapping/Data mapping

Part 11: Concepts: Repository, Meta Data and Maintenance of the Data Model

What is a Repository?
What is Meta Data?
How to maintain the data model?
How to work in a multi-user environment

Data Modeling Demo Videos:

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