Online Data Modeling Training Syllabus

Online Advanced Data Modeling Training on

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

To get more information about this training program, send an email to or call us @ 91-9080157239.

Course Information:

Start Date:
  • Course Duration: 20 Hours; 1.5 Hours per Session; Alternate days.
  • We also offer Training at your convenient time frames. Please call us at 91-9080157239 for more details!
  • Course Name: Advanced Data Modeling Training through GotoMeeting.
  • Course Fee: $195 (One Hundred and Ninety Five US Dollars) or (14,000 Rs) per person in a Batch.
  • Videos: Recorded class videos $50 (3,500 Rs – For those who partake in our Training, Otherwise its $100)
  • Mode of Payment: To USA Savings Account or India Savings Account or through
  • Total no. of theoretical/Practical classes: 20 Hours (Will be extended if required!)
  • Mode of Teaching: Online through GoToMeeting
  • Instructor: Neelesh (US Employee) & Antony (Owner of
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Training Certificates:
  • Will be provided.

Case Study:

  • How to capture Driver, Rider and Ride Information in OLTP/OLAP Data Model for UBER/OLA?
  • How to create OLTP/OLAP/SaaS Data Model for Microsoft Office 365 or similar Software Product?
  • How to create Training Center Data Model for Online/Classroom and Recorded Video Sales?
  • How to create Clinic OLTP/OLAP Data Model from a Receipt?
  • How to create OLTP/OLAP Data Model for Point of Sales (POS) transactions?

Case Study – Exercise:

  • How to create OLTP/OLAP Data Model for 1003 URLA Form?
  • How to create OLTP/OLAP Data Model for Banking/Debit Card Transactions?

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

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?
  • How to design Data Warehouse & Data Mart?
  • Difference Between OLAP Modeling & OLTP Modeling
  • How to resolve the problems found in OLTP & OLAP Modeling?
  • How to design the Dimension & the Fact Tables?
  • What is a Grain Statement & Granularity?
  • Designing using Inmon’s or Kimball’s approach.
  • What is Snow Flake Modeling?
  • What is Star Schema Modeling?
  • Slowly Changing Dimensions – Type I, Type II & Type III
  • What is a Degenerate Dimension?
  • What is Causal Dimension?
  • What is Junk Dimension?
  • What is Outrigger Dimension?
  • 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
  • What is Factless Fact?
  • What is Accumulation Fact?
  • What is Snapshot Fact?
  • What are Additive & Non-Additive and Semi-Additive Measures?
  • Importance of Surrogate Key

Part 11:  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

Part 12:  Introduction to NoSQL Data Modeling

  • Explanation of JSON
  • Explanation of Document Database

Part 13:  Introduction to Big Data:

  • HDFS
  • MapReduce
  • Hadoop
  • Hive
  • Pig Script
  • Yarn
  • ZooKeeper
  • Tez
  • Spark
  • Storm
  • MongoDB
  • HBase
  • MySQL
  • Cassandra

Data Modeling Demo Videos:

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