Data Modeling Online Training

Data Modeling  Online Training By IT Professionals

Prime Online Training offers Data Modeling Online Training. Our Data Modeling trainers are Highly talented and have Excellent Teaching skills. They are well experienced trainers in their relative field. Our online training is one of the Best online training in India For any technology. All our students were happy With our online training and able to find Jobs quickly in USA, UK, Singapore, Japan, Europe. online training is your one stop & Best solution to learn Data Modeling at your home with flexible Timings.

Prime Online Training offers the Data Modeling Online Training Course in a true global setting.

What is Data Modeling?

Data modeling is the act of exploring data-oriented structures.  Like other modeling artifacts data models can be used for a variety of purposes, from high-level conceptual models to physical data models.  From the point of view of an object-oriented developer data modeling is conceptually similar to class modeling. With data modeling you identify entity types whereas with class modeling you identify classes.  Data attributes are assigned to entity types just as you would assign attributes and operations to classes.  There are associations between entities, similar to the associations between classes – relationships, inheritance, composition, and aggregation are all applicable concepts in data modeling.

Prime Online Training offers Data Modeling Online Training. Our Prime trainers are highly talented and well experienced trainers.

Data Modeling Online Training Concepts :


Overview :

• Data Model Defined

• Data Model Introduction

• Conceptual Data Modeling

• Components of Data Model

• Data Modeling Steps

• Quality Of Data Model

• Significance of Data Model Quality

• Data Model Characteristics

• Ensuring Data Model Quality

• Data System Development

• Data System Development Life Cycle

• Roles and Responsibilities of Data Model

• Modeling the Information Requirements

• Applying Agile Modeling Principles

• Data Modeling Approaches and Trends

• Data Modeling Approaches

• Modeling for Data Warehouse

Methods, Techniques, and Symbols

• Data Modeling Approaches

• Semantic Modeling

• Relational Modeling

• ER ( Entity-Relationship ) Modeling

• Methods and Techniques


Anatomy of a Data Model

• Data Model Composition

• Models at Different Levels

• Conceptual Model

• Conceptual Model: Identifying Components

• Creation of Models

• Types of Entities

• Generalization Specialization

• Relationships

• Attributes

• Identifiers

• Review of the Model Diagram

• Overview of Logical Model

• Model Components

• Transformation Steps

• Relational Model

• Overview of Physical Model

• Model Components

• Transformation Steps

Entities in Detail

• Entity Types or Object Sets

• Comprehensive Definition

• Entity Types Identification

• Generalization and Specialization

• Why Generalize or Specialize?

•  Subtypes and Supertypes

• Hierarchy of Generalization

• Recursive Structures

• Conceptual and Physical

•  Time Dimension Modeling

• Categorization

• Entity Validation Checklist

• Completeness and  Correctness

Attributes and Identifiers in Detail

• Attributes

• Properties or Characteristics

• Attributes as Data

• Attribute Values

• Names and Descriptions

• Attribute Domains

• Definition of a Domain

• Domain Information

• Attribute Values and Domains

• Value Set

• Range

• Type

• Null Values

• Types of Attributes

• Single-Valued and Multivalued Attributes

• Simple and Composite Attributes

• Attributes with Stored and Derived Values

•  Keys or Identifiers

• Need of Identifiers

• Definitions of Keys

Relationships in Detail

• Relationships

• Associations

• Relationship: Two-Sided

• Relationship Sets

• Double Relationships

• Relationship Attributes

• Degree of Relationships

• Different Types Of Relationships

• Structural Constraints

• Cardinality Constraints

• Participation Constraints

• Dependencies

• Entity Existence

• Identifying Relationship

• Nonidentifying Relationship

• Maximum and Minimum Cardinalities

• Mandatory Conditions: Both Ends

• Optional Condition: One End

• Optional Condition: Other End

• Optional Conditions: Both Ends

• Special Cases

• Gerund

• Aggregation

• Access Pathways

• Design Issues

• Relationship or Entity Type?

• Ternary Relationship or Aggregation?

• Binary or N-ary Relationship?

• One-to-One Relationships

• One-to-Many Relationships

• Circular Structures

• Redundant Relationships

• Multiple Relationships

• Relationship Validation Checklist

• Completeness

• Correctness

Data Normalization

• Informal Design

• Forming Relations from Requirements

• Potential Problems

• Update Anomaly

• Deletion Anomaly

• Addition Anomaly

• Normalization Methodology

• Strengths of the Method

• Application of the Method

• Normalization Steps

• Overview of Normal Forms

• Normalization Summary

• Review of the Steps

• Normalization as Verification

Modeling for Data warehouse

• Decision-Support Systems

• Need for Strategic Information

• History of Decision-Support Systems

• Operational Versus Informational Systems

• System Types and Modeling Methods

• Data Warehouse

• Data Warehouse Defined

• Major Components

• Data Warehousing Applications

• Modeling: Special Requirements

• Dimensional Modeling

• Dimensional Modeling Basics

• STAR Schema

• Snowflake Schema

• Families of STARS

• Transition to Logical Model

• OLAP Systems

• Features and Functions of OLAP

• Dimensional Analysis

• Hypercubes

• OLAP Implementation Approaches

• Data Modeling for OLAP

• Data Mining Systems

• Basic Concepts

• Data Mining Techniques

• Data Preparation and Modeling

• Data Preprocessing

• Data Modeling

PrimeTrainings provides online training for various courses like

PrimeTrainings provides online training for various courses like DatastageMicrostrategyInformaticaPentaho, QlikviewTeradataBOBICognosOBIEE, , Teradata Development,  Data Modelling 


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