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Online Data Modeling Training – Crash Course

February 8, 2018 learndmdwbi Leave a comment

Crash Course on Data Modeling Training using ERWIN Tool

Course Description:

This 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. To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9884675745.

Course Brochure:

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
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?

Training

Online Data Modeling Training Syllabus

August 22, 2017 learndmdwbi Leave a comment

Online Data Modeling Training on

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

Course# 1 – Learn Erwin data modeling to create Data Models

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 SKYPE or Goto Meeting.

Course Fee:

60$ to 80$ per person

Data Modeling sample used:

Training Institute Data Model

Course Start Time:

Any time.

Course# 2 – Online Project assistance in Data Modeling

Course Description:

Understanding your project requirements, guiding you to create the right data models. Providing Solutions/Assistance to Data Modeling Projects!

Course# 3 – 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. To get more information about this training program, send an email to Training@LearnDataModeling.Com 0r call us @ 91-9080157239.

Course Information:

Data Model Samples Explained:

Bank Data Model
Training Institute Data Model
Online purchase Data Model
Doctor’s clinic Data Model
Insurance Data Model
Mortgage Data Model

Start Date: Batch I starts on 2nd June 2018 & Batch II starts on 23^th June, 2018
Course Name: Advanced Data Modeling Training through SKYPE or 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
Training Hours: 6.30 PM to 8.30 PM Indian Standard Time
Training Time: 2 to 2.5 hours per session
Days: Saturday & Sunday
Total no. of theoretical/Practical classes: At least 12 Hours
Mode of Teaching: Online through SKYPE
Instructor: Neelesh (American Citizen) & Antony (Owner of LearnDataModeling.com)
Office: USA and Chennai

Course Requirements:

Internet connection
Lap Top or Desk Top

Tools:

Erwin
MS Word, MS Excel
My SQL
Windows Operating System

Training Certificates:

Will be provided.

Course Syllabus

Sample Data Models Covered:

Banking
Telecommunications
Staffing etc.,

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 9: 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?

Part 10: 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

Training

Business Intelligence Tutorial

October 15, 2015 learndmdwbi Leave a comment

Business Intelligence & Metadata Tutorial:

Business Intelligence is a terminology refers to taking advantage of data and converting them into an intelligent information or knowledge by carefully observing data patterns or trends. These findings are key factors in helping any business to improve it’s current business processes to gain more on customer satisfaction, increase sales, produce more profit etc. The knowledge observed from several report based analysis may lead to new business changes or improvements thus helping the organization to grow in the targeted direction. Browse through the various topics listed below to know more.

Advantages of Multi Dimensional Databases

October 15, 2015 learndmdwbi Leave a comment

OLAP Database – Multidimensional:

This is a type of database that is optimized for data warehouse, data mart and online analytical processing (OLAP) applications. The main advantage of this database is query performance.

Relational databases make it easy to work with individual records, whereas multidimensional databases are designed for analyzing large groups of records. Relational database is typically accessed using a Structured Query Language (SQL) query. A multidimensional database allows a user to ask questions like “How many mortgages have been sold in New Jersey city” and “How many credit cards have been purchased in a particular county?”.

Popular Multidimensional Databases:

Database Name	Company Name
Crystal Holos	Business Objects
Hyperion Essbase	Hyperion
Oracle Express	Oracle Corporation
Oracle OLAP Option	Oracle Corporation
AWMicrosoft Analysis Services	Microsoft
PowerPlay Enterprise	Cognos

OLAP and OLAP Hybrids

October 15, 2015 learndmdwbi Leave a comment

OLAP & its Hybrids:

OLAP, an acronym for Online Analytical Processing is an approach that helps organization to take advantages of DATA. Popular OLAP tools are Cognos, Business Objects, Micro Strategy etc. OLAP cubes provide the insight into data and helps the topmost executives of an organization to take decisions in an efficient manner.

Technically, OLAP cube allows one to analyze data across multiple dimensions by providing multidimensional view of aggregated, grouped data. With OLAP reports, the major categories like fiscal periods, sales region, products, employee, promotion related to the product can be ANALYZED very efficiently, effectively and responsively. OLAP applications include sales and customer analysis, budgeting, marketing analysis, production analysis, profitability analysis and forecasting etc.

ROLAP:

ROLAP stands for Relational Online Analytical Process that provides multidimensional analysis of data, stored in a Relational database (RDBMS).

MOLAP:

MOLAP (Multidimensional OLAP), provides the analysis of data stored in a multi-dimensional data cube.

HOLAP:

HOLAP (Hybrid OLAP) a combination of both ROLAP and MOLAP can provide multidimensional analysis simultaneously of data stored in a multidimensional database and in a relational database (RDBMS).

DOLAP:

DOLAP (Desktop OLAP or Database OLAP)provide multidimensional analysis locally in the client machine on the data collected from relational or multidimensional database servers.

OLAP Analysis

October 15, 2015 learndmdwbi Leave a comment

OLAP Analysis:

Imagine an organization that manufactures and sells goods in several States of USA which employs hundreds of employees in its manufacturing, sales and marketing division etc. In order to manufacture and sell this product in profitable manner, the executives need to analyse (OLAP analysis) the data on the product and think about various possibilities and causes for a particular event like loss in sales, less productivity or increase in sales over a particular period of the year.

During the OLAP analysis, the top executives may seek answers for the following:

1. Number of products manufactured.

2. Number of products manufactured in a location.

3. Number of products manufactured on time basis within a location.

4. Number of products manufactured in the current year when compared to the previous year.

5. Sales Dollar value for a particular product.

6. Sales Dollar value for a product in a location.

7. Sales Dollar value for a product in a year within a location.

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

OLAP tools help executives in finding out the answers, not only to the above mentioned measures, even for the very complex queries by allowing them to slice and dice, drill down from higher level to lower level summarized data, rank, sort, etc.

Example of OLAP Analysis Report:

Time Dimension Id	Location Dimension Id	Product Dimension Id	Organization Dimension Id	Sales Dollar	DateTimeStamp
1	1	100001	1	1000	1/1/2005 11:23:31 AM
3	1	100001	1	750	1/1/2005 11:23:31 AM
1	1	100001	2	1000	1/1/2005 11:23:31 AM
3	1	100001	2	750	1/1/2005 11:23:31 AM

In the above example of OLAP analysis, data can be sliced and diced, drilled up and drilled down for various hierarchies like time dimension, location dimension, product dimension, and organization dimension. This would provide the topmost executives to take a decision about the product performance in a location/time/organization. In OLAP reports, Trend analysis can be also made by comparing the sales value of a particular product over several years or quarters.

Business Intelligence Tools

October 15, 2015 learndmdwbi Leave a comment

Business Intelligence Tools:

Business Intelligence Tools help to gather, store, access and analyze corporate data to aid in decision-making. Generally these systems will illustrate business intelligence in the areas of customer profiling, customer support, market research, market segmentation,
product profitability, statistical analysis, inventory and distribution analysis.

With Business Intelligence Tools, various data like customer related, product related, sales related, time related, location related, employee related etc. are gathered and analysed based on which important strategies or rules are formed and goals to achieve their target are set. These decisions are very efficient and effective in promoting an Organisation’s growth.

Since the collected data can be sliced across almost all the dimensions like time,location, product, promotion etc., valuable statistics like sales profit in one region for the current year can be calculated and compared with the previous year statistics.

Popular Business Intelligence Tools:

Tool Name	Company Name
Business Objects	Business Objects
Cognos	Cognos
Hyperion	Hyperion
Microstrategy	Microstrategy
Microsoft Reporting Services	Microsoft
Crystal	Business Objects

Advantages of Business Intelligence

October 15, 2015 learndmdwbi Leave a comment

Business Intelligence:

Business Intelligence is a technology based on customer and profit oriented models that reduces operating costs and provide increased profitability by improving productivity, sales, service and helps to make decision making capabilities at no time. Business Intelligence Models are based on multi dimensional analysis and key performance indicators (KPI) of an enterprise.

Business Intelligence applications that are based on Business Intelligence Models are created by Business Intelligence software which provides the aggregated details about suppliers, customers, internal activities, business to business transactions to the managers or whoever needs it to take better corporate decisions.

Many business questions or situations need to be analyzed in order to achieve the target of an enterprise with the help of several managers or executives in each cadre. Below are some of the samples of these questions.

» Business Intelligence in Finance:

What is the net income, expenses, gross profit, and net profit for this quarter/year?

» Business Intelligence in Accounts:

What is the sales amount this month and what is the outstanding pending payment?

» Business Intelligence in Purchase:

Who is the vendor to be contacted to purchase products?

» Business Intelligence in Production:

How many products are manufactured in each production unit today/weekly/monthly?

» Business Intelligence in Sales:

How many products have been sold in each area today/weekly/monthly?

» Business Intelligence in Quality:

How many products have been defective today/weekly/monthly/quarterly/yearly?

» Business Intelligence in Service:

Are the customers satisfied with the quality?

These business intelligence questions are related with why, what, how, when, and business intelligence reports (olap reports) are useful in providing solutions to the above questions by means of reporting, score cards, balance score cards that are helpful in managerial decisions.

Metadata Reports

October 15, 2015 learndmdwbi Leave a comment

Metadata Reports:

Metadata stored in a repository can be produced in the form of reports for easy understanding and these reports are very useful in explaining about the various objects or data structures and the relationship between these objects. The following products like Schema Logic Enterprise Suite, Rochade, Metatrieve, Datamapper, Metacenter, Metadata Integration Frame work stores and handles metadata in an efficient and effective manner.

Following are few examples for metadata reports.

A metadata report on business process activities, data flow.
A metadata report on source and target columns can be generated to find out all the source and target field names, data types, definitions etc.
metadata report on workflow can be generated to find out all the workflows, workflow properties, workflow scheduling, workflow execution details etc.
A metadata report on mapping can be generated to find out all the sources and targets in the mapping, source fields and target fields participated in the mapping etc.
A metadata report on business intelligence can be generated to find out all the score cards and balanced score cards.

Role of Metadata in ETL

October 15, 2015 learndmdwbi Leave a comment

Role of Metadata in ETL:

When you deal with a data warehouse, various phases like Business Process Modeling, Data Modeling, ETL, Reporting etc., are inter-related with each other and they do contain their own metadata. For example in ETL, it will be very difficult for one to extract, transform and load source data into a data warehouse, if there is no metadata available for the source like where and how to get the source data.

Let us explain the role of metadata in the ETL process with the help of an example table shown below which contains information about an organisation’s employees.

Employee Name	Employee Age	Employee Salary	Employee Title
John Hick	36	$3000	Informatica Specialist

In the above table, the second row, containing information like John Hick, 36, $3000, Informatica Specialist are known as Data. Whereas the first row, (i.e) table header containing headings like Employee Name, Employee Age, Employee Salary, Employee Title are called as Metadata for the above said data.

An organization may be using data modeling tools, such as Erwin, Embarcadero, Oracle designer, Sybase Power Designer etc., for developing data models. Functional and technical team should have spent much time and effort in creating the data model’s data structures (tables, columns, data types, procedures, functions, triggers etc.,). By using metadata capturing tools, these data structures can be imported into metadata repository which we call it as metadata.

For example when you work with Informatica’s Metadata Exchange, it captures the metadata present in these tools and loads into the repository. There is no need for Informatica developer to create these data structures once again since metadata(data definitions) have been already captured and stored. Similarly most of the ETL tools have that capability to capture metadata from RDBMS, files, ERP, Applications etc.

In ETL, Metadata Repository is where all the metadata information about source, target, transformations, mapping, workflows, sessions etc., are stored.
From this repository, metadata can be manipulated, queried and retrieved with the help of wizards provided by metadata capturing tools. During the ETL process, when we are mapping source and target systems, we are actually mapping their metadata.

In any organization, a useful metadata often stored in a repository can be a handy resource to know about the organization’s information systems. Assume that each department in an organization may have a different business definitions, data types, attribute names for the same attribute or they may have a single business definition for many attributes. These anomalies can be overcome by properly maintaining metadata for these attributes in the centralized repository.

Thus metadata plays a vital role in explaining about how, why, where data can be found, retrieved, stored and used efficiently in an information management system.

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