Online Data Modeling Training Syllabus
Online Advanced Data Modeling Training on
OLTP, Data Warehouse, Datamart, Dimensional and Snow Flake Data Modeling and Normalization.
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 AntonysTrainingandSolution@gmail.com or call us @ 91-9080157239.
- Starts on 22nd April 2019
- Course Name: Advanced Data Modeling Training through GotoMeeting.
- Course Fee: $195 (One hundred and Ninety five US Dollars) per person
- Mode of Payment: To USA Savings Account or India Savings Account or through www.Xoom.com
- 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 LearnDataModeling.com)
- Office: USA and Chennai
- Internet connection
- Lap Top or Desk Top
- Will be provided.
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
- 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: Data Modeling Training on Big Data:
While I write this, one should not think that this is “Big Data” Data Modeling. What I mean to write here is how to model the “big data”, which has very big data/huge volume/high velocity by using OLTP and OLAP Data Modeling.
Data Modeling Demo Videos:
- 01 Data Modeling Development Life Cycle
- 02 Data Warehouse Training – Table & PK
- 03 Simple Select Statement and Alter Statements
- 04 Data Warehouse Training ETL Tools
- 05 Data Warehouse Training Normalization
- 06 Data Warehouse Training – Cardinality and Optionality
- 07 Sale – OLTP AND Data Modeling Training Videos with Erwin