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: 6th September, 2018.
  • Mode of Training: Online Through GotoMeeting.
  • Course Fee: $125 (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: 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.

Oracle DDL Commands – Create Commands

Oracle Database Data Definition Language (DDL Statements) – Create Commands

To execute Oracle database commands, you need Oracle’s username, password and oracle instance name to connect to oracle database. If you haven’t got the username, contact database administrator (DBA) and get the relevant privileges. When you are working with a client, you may be provided a development schema to execute all of your database commands or privileges granted to work on all schemas depending upon your data modelling activity. All commands should be executed from sql> prompt and “;” indicates the end of an oracle command.

An Oracle database consists of DDL commands, which are useful to create, modify and drop the database objects. In this section, we will try to explain about important database CREATE commands that are used by a data modeler by relating it with our example data.

Create Table “GENDER_LKP”:

Create table statements are used for creating tables by which data is stored permanently in the database.

CREATE TABLE GENDER_LKP (

GNDR_CD VARCHAR2 (10) NOT NULL,

GNDR_DESC VARCHAR2 (50) NOT NULL,

DTTM_STMP DATE NOT NULL,

CONSTRAINT GENDER_LKP_PK PRIMARY KEY (GNDR_CD)

);

Create Table “DEPARTMENT_LKP”:

CREATE TABLE DEPARTMENT_LKP (

DPTMT_NUM NUMBER(2) NOT NULL,

DPTMT_DESC VARCHAR2(50) NOT NULL,

DTTM_STMP DATE NOT NULL,

CONSTRAINT DEPARTMENT_LKP_PK

PRIMARY KEY (DPTMT_NUM)

);

Create Table “EMPLOYEE_DTL”:

CREATE TABLE EMPLOYEE_DTL (

EMP_DTL_ID NUMBER NOT NULL,

GNDR_CD VARCHAR2(10) NOT NULL,

DPTMT_NUM NUMBER(2) NULL,

FRST_NM VARCHAR2(30) NOT NULL,

LST_NM VARCHAR2(30) NOT NULL,

MDLE_NM VARCHAR2(30) NULL,

BRTH_DT DATE NOT NULL,

SSN VARCHAR2(11) NOT NULL,

SLRY_AMT NUMBER(7,2) NOT NULL,

DTTM_STMP DATE NOT NULL,

CONSTRAINT EMPLOYEE_DTL_PK PRIMARY KEY (EMP_DTL_ID),

CONSTRAINT EMPLOYEE_DTL_FK01 FOREIGN KEY (DPTMT_NUM) REFERENCES DEPARTMENT_LKP,

CONSTRAINT EMPLOYEE_DTL_FK02 FOREIGN KEY (GNDR_CD) REFERENCES GENDER_LKP

); 

 How to copy a table with data?

As of now, table EMPLOYEE_DTL contains no data. As soon as you load the data into EMPLOYEE_DTL, try the following command.

CREATE TABLE EMPLOYEE_DTL_COPY AS SELECT * FROM EMPLOYEE_DTL;

How to copy a table with no data?

CREATE TABLE EMPLOYEE_DTL_COPY AS SELECT * FROM EMPLOYEE_DTL WHERE 1=2; 

How to create a Sequence?

This sequence is used to generate unique numbers for the column ‘EMP_DTL_ID’.

CREATE SEQUENCE SEQ_EMPLOYEE_DTL

INCREMENT BY 1

START WITH 1

NOMAXVALUE

NOMINVALUE

NOCACHE

NOCYCLE

NOORDER

;

How to create a Trigger?

Whenever a record is inserted into “EMPLOYEE_DTL” table, this trigger selects the next unique number from the sequence “SEQ_EMPLOYEE_DTL” and inserts into the column “EMP_DTL_ID”.

In our INSERT STATEMENTS example, we have not provided values for the column “EMP_DTL_ID” and inserting values into “EMP_DTL_ID is taken care by sequence and trigger.

CREATE OR REPLACE TRIGGER TRG_SEQ_EMPLOYEE_DTL

BEFORE INSERT ON EMPLOYEE_DTL

FOR EACH ROW

BEGIN

SELECT SEQ_EMPLOYEE_DTL.NEXTVAL INTO :NEW.EMP_DTL_ID FROM DUAL;

END;

How to create an Index?

CREATE INDEX IND_SSN ON EMPLOYEE_DTL(SSN); 

How to create a View?

CREATE VIEW VIEW_EMPLOYEE_DTL AS SELECT * FROM EMPLOYEE_DTL; 

How to create a Synonym?

CREATE SYNONYM SYN_EMPLOYEE_DTL FOR EMPLOYEE_DTL; 

Database: Sample Data Analysis

Database: Sample Data

With a simple example of designing a data model with ‘Employee related information’, most of the data structures can be easily understood. So we will try to design a data model using the sample data given below and will explain the data modeler’s involvement in the database environment.

Sample Source Data:

First Name Middle Name Last Name SSN Gender Birth Date Salary Dept. No Dept. Desc
Melinda J Schipper 765-43-21 Female 20-APR-1973 5000
Kevin A Schulte 123-45-67 Male 13-OCT-1969 5000 10 IT

Note: If you go through our topics provided under the Data Modelling Section, you will have a brief idea about how to design a data model.

Database: Sample Data Analysis

For the sample data provided in the above table, as a Data Modeler you have to design logical data model, physical data model and generate DDL scripts. In order to do the above tasks, you need to create the following:

  • Create 3 tables GENDER_LKP, DEPARTMENT_LKP, EMPLOYEE_DTL.
  • Assign correct data types to the columns.
  • Create constraints like primary key, unique key, null, not null, check to the columns.
  • Assign correct PHYSICAL names for tables and columns.
  • Select GENDER_LKP, DEPARTMENT_LKP as parent tables and EMPLOYEE_DTL as the child table and connect them.
  • Even though column ‘DTTM_STMP’ has not been present in the sample data, you have to add ‘DTTM_STMP’ to all tables to know the date and time on which a record is inserted or updated.

GENDER_LKP Analysis:

  • Since ‘male’ and ‘female’ values will be repeated for several records in EMPLOYEE_DTL table, you have to design this lookup for saving disk space. Sometimes the detail table may not show the exact business requirements also.
  • E.g. Since the example data contains both genders; you know that there are two genders. What would have happened if all records were male? By seeing the sample data, you might have come to a conclusion that there are only ‘males’ allowed as employees. By discussing with Business Analyst, you would come to know that both genders are allowed. For this purpose, you design a lookup table, which identifies the business requirements also.
  • You have to assign NOT NULL constraint for all columns since lookup tables in general should not have null value.
  • You have to create a column Gender Code, which is not present in sample data and this column should be assigned Primary Key to validate the detailed data in EMPLOYEE_DTL table.

Sample Source Data:

 

Entity Name Table

Name

Att

Name

Col

Name

Data

type

Null Option P Key F Key
Gender Lookup GENDER_LKP Gender Code GNDR_CD VARCHAR2(10) NOT NULL Yes No
Gender Lookup GENDER_LKP Gender Description GNDR_DESC VARCHAR2(50) NOT NULL No No
Gender Lookup GENDER_LKP DateTime Stamp DTTM_STMP DATE NOT NULL No No

Analysis of DEPARTMENT_LKP:

  • Since ‘Dept No’ and ‘Dept Desc’ values will be repeated for several records in EMPLOYEE_DTL table, you have to design this lookup for saving the disk space.
  • You have to assign NOT NULL constraint for all columns. Generally lookups should not have null value.
  • You have to assign ‘Dept No’ as the primary key to validate the detailed data in EMPLOYEE_DTL table.

Sample Source Data:

Entity Name Table

Name

Att

Name

Col

Name

Data

type

Null Optn P Key F Key
Department Lookup DEPART

MENT_LKP

Depart

ment Number

DPTMT_NUM NUMBER(2) NOT NULL Yes No
Department Lookup DEPART

MENT_LKP

Depart

ment Description

DPTMT_DESC VARCHAR2(50) NOT NULL No No
Department Lookup DEPART

MENT_LKP

DateTime Stamp DTTM_STMP DATE NOT NULL No No

Analysis of EMPLOYEE_DTL:

  • As of now, this example entity contains only sample of 2 records. In real time, this entity would store millions of records. In order to retrieve the data in a faster way, you have to create an additional column EMP_DTL_ID in EMPLOYEE_DTL and assign it as primary key.
  • In our example, column EMP_DTL_ID will be populated through sequence initiated by a trigger and you can see the sequence code and trigger code in later sections.
  • Even though SSN can be added as a primary key in EMPLOYEE_DTL table, you have to add a new column EMP_DTL_ID as the primary key to ensure the fastest retrieval of data.
  • Tip: Wherever it is required, you have to create new columns.

Sample Source Data:

Entity Name Table

Name

Att

Name

Col

Name

Data

type

Null Option P Key F Key
Employee Detail EMPLOYEE

_DTL

Employee Detail Identifier EMP_DTL_ID NUMBER NOT NULL Yes No
Employee Detail EMPLOYEE

_DTL

Department Number DPTMT_NUM NUMBER(2) NULL No Yes
Employee Detail EMPLOYEE

_DTL

Gender Code GNDR_CD VARCHAR2(10) NOT NULL No Yes
Employee Detail EMPLOYEE

_DTL

First Name FRST_NM VARCHAR2(30) NOT NULL No No
Employee Detail EMPLOYEE

_DTL

Last Name LST_NM VARCHAR2(30) NOT NULL No No
Employee Detail EMPLOYEE

_DTL

Middle Name MDLE_NM VARCHAR2(30) NULL No No
Employee Detail EMPLOYEE

_DTL

SSN SSN VARCHAR2(11) NOT NULL No No
Employee Detail EMPLOYEE

_DTL

Birth Date BRTH_DT DATE NOT NULL No No
Employee Detail EMPLOYEE

_DTL

Salary Amount SLRY_AMT NUMBER(7,2) NOT NULL No No
Employee Detail EMPLOYEE

_DTL

DateTime Stamp DTTM_STMP DATE NOT NULL No No

 

Oracle Database Objects Overview

Oracle Database Objects Overview:

A database can have many schemas; one schema can contain multiple database objects like tables, views, Synonym etc. A brief explanation on each of these Oracle database objects is given below. For more detailed explanations, please refer the official website of Oracle at www.oracle.com.

Schema:

This is also known as USER and is a collection of database objects and as a data modeler one should know how to login into a particular schema and to manage these database objects.

Table:

A set of related data, arranged in the form of rows and columns.

Column:

This is also known as Field that provides the structure for organizing the rows and contains the related information.

Data type:

This is set of property associated with a column, which helps to store and identify the type of data and its length.

Null:

This is a value that indicates that the column contains no valid data.

Not Null:

This is a constraint that indicates that the column should contain data.

Primary Key Constraint:

This is a constraint imposed on the column so that all values in the column should be different from each other. This constraint can be imposed on one column or group of columns. The primary key will be always used as a parent key when adding a referential constraint by connecting it to a child table.

  • Unique Constraint: Unique + Null Values
  • Primary Key Constraint: Unique + Not Null Values

Foreign Key Constraint:

This is a constraint imposed on the child table. Whatever values are present in the child table, their corresponding values should be present in the parent table. This constraint can be imposed on one column or group of columns and NULL values are allowed in the child table.

Unique Constraint:

This is a constraint imposed on a column so that all NON-NULL values in the column should be different from each other. This constraint can be imposed on one column or group of columns.

Check Constraint:

This is a constraint that is imposed to validate the data within some value or range of values. This constraint can be imposed on one column or group of columns.

Index:

Index is a database object that enables faster retrieval of data. Unique Index, Bitmap Index etc., are the different types of Index.

Sequence:

This is a database object that generates unique numbers.

View:

This is a PSEUDO table that is not stored in the database and it is just a query.

Materialized Views:

Materialized Views are similar to a view but these are permanently stored in the database and often refreshed. This is used in optimization for the faster data retrieval and is useful in aggregation and summation of data.

Synonym:

This is an alias name for the object in the database created with CREATE SYNONYM command.

Procedure:

This is a program that contains set of code, which will carry out a specific action when called by other programs.

Function:

This is a program that contains set of code, which will do a specific action when called by other programs.

Package:

This is a collection of procedures, functions, PL/SQL tables, etc., that contains set of code, which will do a specific action when called by other programs.

Trigger:

This is a program that contains set of code for doing some useful action when a record is inserted or deleted or updated in a table.

 

Database Overview

Database Overview:

A database is a collection of organized and structured data, stored in the computer as files. Various data types like numeric, textual, image, multimedia etc., can be managed and maintained more efficiently in a database.

Database Types:

  • Database Management Systems (DBMS)
  • Relational Database Management Systems (RDBMS)
  • Object Oriented Databases
  • Multidimensional Databases

Often used databases (RDBMS) in most of the practical applications are Oracle, Sql Server, Informix, Teradata, DB2 etc., and in the following pages, Oracle’s data structures are used as examples to explain the relationship between data modelling and database. In order to design a data model in a proper manner, a data modeler has to know the different objects (data structures) present in a database. Also data modeler should have a sound knowledge of the data present/to be present in the database, should be able to design a data model using a data modelling tool like Erwin, and to generate DDL scripts from the Data Modelling tool.

Given below is the list of Oracle data objects and in the following pages, a brief overview is given for each of these objects.

Oracle Database Objects:

  • Instance
  • Schema
  • Table
  • Column
  • Data type
  • Primary Key Constraint
  • Unique Constraint
  • Check Constraint
  • Null
  • Not Null
  • Index
  • Sequence
  • View
  • Materialized View
  • Synonym
  • Procedure
  • Function
  • Package
  • Trigger

 

Online Data Modeling Training – Crash Course

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 using Erwin, Power Designer & Oracle SQL Data Modeler.

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?

 

Data Modeling Demo Videos:

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 26th September 2018 (Daily Classes – 10.00 P.M EST to 11.00 P.M EST)
  • 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
  • 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:

Business Intelligence Tutorial

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 organisation to grow in the targeted direction. Browse through the various topics listed below to know more.

Metadata & Business Intelligence:



Advantages of Multi Dimensional Databases

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 NameCompany Name
Crystal HolosBusiness Objects
Hyperion EssbaseHyperion
Oracle ExpressOracle Corporation
Oracle OLAP OptionOracle Corporation
AWMicrosoft Analysis ServicesMicrosoft
PowerPlay EnterpriseCognos

 

OLAP and OLAP Hybrids

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.

 

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