Dimensional Data Modeling Training Online

Online Dimensional Data Modeling Training

June 2, 2018 learndmdwbi 3 comments

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

Data Modeling Course Syllabus & Feedback:

Data Modeling Course Feedback & Testimonials:

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 AntonysTrainingAndSolution@gmail.com or call us @ 91-9080157239.

Course Information:

We offer Training at your convenient time slots! Please call us at 91-9080157239 for more details!
Mode of Training: Online Through GotoMeeting.
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
Instructor: Neelesh (US Employee) & Antony (Owner of LearnDataModeling.com)
Office: USA and Chennai

Course Requirements:

Internet connection
Lap Top or Desk Top

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
What is Causal Dimension?
What is Junk Dimension?
What is Outrigger Dimension?

Part 6 – Designing Star Schema:

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 1
Type 2
Type 3

Part 8 – 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.

Popular/Top Data Modeling Tools in the Market:

Erwin
E/R Studio
SAP PowerDesigner
Toad Data Modeler
Oracle SQL Developer Data Modeler
MySQL Workbench
IBM InfoSphere Data Architect

Interview Question & Answer Videos:

Data Modeling Interview Question & Answer Videos

Online Training Start Date: 9th January, 2023.

Training

Dimensional Data Modeling

June 18, 2015 learndmdwbi Leave a comment

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.

Data Modeling Types