Online Dimensional Data Modeling Training

Online Dimensional Data Modeling Training | Data Warehouse Training | Data Mart Training

Course Description:

The dimensional data modeling training explains how to design Data Ware House and Data Marts from OLTP data models using Erwin (OR) Power Designer (OR) Oracle SQL Data Modeler!

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Course Information:

 

  • Training Start Date: 24th November, 2018. (Saturday & Sunday)
  • Mode of Training: Online Through GotoMeeting.
  • Course Fee: $150 (One hundred and fifty 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: At least 10 Hours (Will be extended if required)
  • Course Materials: Course Material will be Provided
  • Videos: Training Session Videos will be Provided
  • Instructor: Neelesh (US Employee) & Antony (Owner of LearnDataModeling.com)
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Tools:
  • Erwin
  • Power Designer
  • Oracle SQL Data Modeler
  • MS Word, MS Excel
  • My SQL
  • Windows Operating System
Training Certificates:
  • Will be provided.

Course Syllabus

Training Data Model Samples:
  • POS (Point of Sales Data Model)
  • Banking Data Model
  • Mortgage Data Model
Part 1 – Dimensional Data Modeling (OLAP):
  • Need for Strategic Information
  • Examples of Business Objectives
  • Characteristics of Information
  • Operational Systems – OLTP
  • Examples of Operational Systems
  • Decision Support Systems – OLAP
  • Operational VS Decision Support
Part 2 – Introduction to Data Warehouse:
  • Data Warehouse Definition
  • Data Marts
  • Data Warehouse and Data Mart
  • Pioneer of Data Warehousing (Inmon)
  • Bill Inmon’s Approach (HUB and Spoke Architecture)
  • Pros and Cons of Inmon’s Approach
  • Pioneer of Data WareHousing (Ralph Kimball)
  • Ralph Kimball’s Approach (Bus Architecture)
  • Pros and Cons of Kimball’s Approach
  • What is ETL?
  • Things to learn for mapping/Data mapping
Part 3 – Business Intelligence:
  • OLAP
  • Dimensional Modeling
Part 4 – Different types of Fact Tables:
  • Transactional Facts
  • Snapshot or inventory
  • Factless Facts
  • Semi Additive and Non Additive Facts
Part 5 – Dimension Tables:
  • Dimension Types
  • Degenerate Dimensions
  • Denormalized Flattened Dimensions
  • Snowflaked Dimensions
  • Role-Playing Dimensions
  • Junk Dimensions
  • Outrigger Dimensions
Part 6 – Designing Star Schema:
  • Putting Building Blocks Together
  • Dimensional Model
  • Dimensional (Star) Schema
  • Declare the grain of Business Process
  • 4 Steps Dimensional Model Design Process
    • Identify Business Process
    • Identify Grain
    • Identify Dimensions
    • Identify Facts
Part 7 – Slowly Changing Dimension Techniques:
  • Type 0: Retain Original
  • Type 1: Overwrite
  • Type 3: Add New Attribute
  • Type 4: Add Mini-Dimension
  • Type 5: Add Mini-Dimension and Type 1 Outrigger
  • Type 6: Add Type 1 Attributes to Type 2 Dimension
  • Type 7: Dual Type 1 and Type 2 Dimensions
Part 7 – Big Data Data Modeling:
  • Design data model of large volumes of Data (Large Volumes of Data) with RDBMS structure

 

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Online Data Modeling Training Syllabus

Online Data Modeling Training on

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

 

Course# 1 – Learn Erwin or SAP PowerDesigner or Oracle Data Modeler

Course Description:

This online course explains how to use Erwin Data Modeling tool to create logical data model, conceptual data model and physical data model. It also explains how to create different objects like entity, attribute, relationship, null, not null, primary key, foreign keys, naming conventions, one to one relationship, one to many relationship, many to many relationship, identical relationship, non-identical relationship, default, domain, subject area, reports generation etc.

Course Duration:

3 hours to 4 hours through Goto Meeting.

Data Modeling sample used:

Training Institute Data Model

Course Start Time:

Any time.

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.

If you are fresher or a beginner, we will teach the fundamental concepts on one-to-one basis and later you will be enrolled in Course# 2 i.e Online Advanced Data Modeling Training.

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Course Information:
Start Date:
  • Batch starts on 10th November 2018 (Saturday & Sunday)
  • Course Name: Advanced Data Modeling Training through GotoMeeting.
  • Course Fee: $175 (One hundred and seventy 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
  • Mode of Teaching: Online through GoToMeeting
  • Instructor: Neelesh (US Employee) & Antony (Owner of LearnDataModeling.com)
  • Office: USA and Chennai
Course Requirements:
  • Internet connection
  • Lap Top or Desk Top
Tools:
  • Erwin
  • Power Designer
  • Oracle SQL Data Modeler.
  • MS Word, MS Excel
  • My SQL
  • Windows Operating System
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:  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:

Dimensional Data Modeling

Dimensional Data Modeling comprises of one or more dimension tables and fact tables. Good examples of dimensions are location, product, time, promotion, organization etc. Dimension tables store records related to that particular dimension and no facts (measures) are stored in these tables.

For example, Product dimension table will store information about products (Product Category, Product Sub Category, Product and Product Features) and location dimension table will store information about location ( country, state, county, city, zip. A fact (measure) table contains measures (sales gross value, total units sold) and dimension columns. These dimension columns are actually foreign keys from the respective dimension tables.

Example of Dimensional Data Model:

Example of Dimensional Data Model

In the example figure above, sales fact table is connected to dimensions location, product, time and organization. It shows that data can be sliced across all dimensions and again it is possible for the data to be aggregated across multiple dimensions. “Sales Dollar” in sales fact table can be calculated across all dimensions independently or in a combined manner which is explained below.

  • Sales Dollar value for a particular product.
  • Sales Dollar value for a product in a location.
  • Sales Dollar value for a product in a year within a location.
  • Sales Dollar value for a product in a year within a location sold or serviced by an employee

In Dimensional data modeling, hierarchies for the dimensions are stored in the dimensional table itself. For example, the location dimension will have all of its hierarchies from country, state, county to city. There is no need for the individual hierarchical lookup like country lookup, state lookup, county lookup and city lookup to be shown in the model.

Dimensional Data Modeling is used for calculating summarized data. For example,
sales data could be collected on a daily basis and then be aggregated to the week level, the week data could be aggregated to the month level, and so on. The data can then be referred to as aggregate data. Aggregation is synonymous with summarization, and aggregate data is synonymous with summary data. The performance of dimensional data modeling can be significantly increased when materialized views are used. Materialized view is a pre-computed table comprising aggregated or joined data from fact and possibly dimension tables which also known as a summary or aggregate table.

Online Dimensional Data Modeling Training | Data Warehouse Training | Data Mart Training

Course Description:

The dimensional data modeling training explains how to design Data Ware House and Data Marts from OLTP data models.

What you can learn in our Dimensional Data Modeling training?

  • Why do you need a data warehouse?
  • What is the reason for a data mart?
  • Why is star schema? How is that different from dimensional data modeling?
  • Why customers prefer dimensional data modeling or snow flake modeling?
  • What are slowly changing dimensions?

To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.