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?

 

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 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.

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, Power Designer & 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 I starts on 30th July, 2018 (Daily Classes – 9.15 P.M EST to 10.15 P.M EST)
  • Batch II starts on 13th August 2018 (Daily Classes – 9.15 P.M EST to 10.15 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: At least 14 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.

 

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

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.

 

OLAP Analysis

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 IdLocation Dimension IdProduct Dimension IdOrganization Dimension IdSales DollarDateTimeStamp
11100001110001/1/2005 11:23:31 AM
3110000117501/1/2005 11:23:31 AM
11100001210001/1/2005 11:23:31 AM
3110000127501/1/2005 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

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 NameCompany Name
Business ObjectsBusiness Objects
CognosCognos
HyperionHyperion
MicrostrategyMicrostrategy
Microsoft Reporting ServicesMicrosoft
CrystalBusiness Objects

 

Advantages of Business Intelligence

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.

 

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