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Information System

The aim of information system is to support operations and

decision making.

Types of Information system: Online transaction processing(OLTP)

Data Warehouse(DW)

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Online transaction processing System

OLTP contains commercial transaction or operational data.

It is a up to date system.

Database updates whenever a new transaction is performed.

OLTP system stores data of few weeks or months.

OLTP have normalized schema to optimize the DML

statement.

Example: Credit card transaction, Reservation system, booking

system, billing system, accounting system, banking system

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Data Warehouse System

The Data warehouse is :

Subject oriented

Integrated

Time variant

Non volatile

Collection of data which is used for decision making purpose.

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Data Warehouse System(Contd.)

DW system contains historical data.

It is not a up to date system.

Database updates on daily or weekly basis.

DW system stores many years of data.

DW have Denormalized schema.

It is used for reporting purpose.

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DW Architecture (ETL with stage area)

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Why Staging area is required

Single platform for all sources.

Optimize performance of ETL.

Do not hamper the performance of OLTP system.

Easily restart load after failure.

Simplified the job of testing in case of XML or Cobol source

data

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ODS layer

ODS layer presents between the OLTP and DW system.

ODS is a replica of your OLTP systems.

suppose you want to analyze live data or current data then we

use ODS layer.

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Dimensional modeling

In DW we use Dimensional modeling but in OLTP we use ER

modeling.

Terms used in dimension modeling are-

Dimension

Attribute

Fact

Types of Schema to represent a dimensional model

Star Schema

Snowflake Schema

Fact Constellation

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Dimension

Dimension is used to categorize the data.

Dimension has attributes.

All dimension has one primary key.

Example for sales data warehouse dimension would beproduct, customer, time and store.

Product

Product_ID

NameExpiry _date

Mfr_date

Manufacturer

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Conformed Dimension

A dimension having same structure in all data marts.

Create it only once and refer it in all data marts.

Example: Product dimension is used in sales and inventory

data mart. It has same structure in both data marts.

Product

Product_id

Product_name

Mfg_date

Expiry_date

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Slowly Changing Dimension

In SCD attributes are changed with time .

Example in customer dimension address, email id and contact

no can be changed with time.

Types of Dimension

SCD I

SCD II

SCD III

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Slowly Changing Dimension I

Always Update records if already present.

History is not maintained.

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Slowly Changing Dimension II

Create a new row for each record. No updation only insertion.

History is maintained.

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Slowly Changing Dimension III

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Fact table

Fact tables contains measures and numerical data.

It represent a value with respect to a combination of all

dimension.

A fact table has foreign key for all dimensions.

A fact table has a primary key to represent unique transaction.

Example sales table

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Fact table(Contd.)

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Factless fact table

A factless fact table doesnt contain any measures.

For example Student attendance

Fact_Attendance

Student_id

Class_id

Time_id

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Junk Dimension

Junk dimension is a group of low cardinality attributes.

Example Voucher_ind and payback_ind

Sales fact table

Product_id

Customer_id

Txn_id

Store_id

Voucher_indPayback_ind

Txn_AMT

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Junk Dimension(Contd.)

There are two ways to handle low cardinality attributes.

First way is two create two dimension for each of attribute.

Second way: use junk dimension.

First way is not a good practice because if no of indicators

Increases the no of dimension tables will also increase.

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Junk Dimension(Contd.)

Junk_id Voucher_id Payback_Ind

1 Y N

2 N Y

3 N N

4 Y Y

Sales fact table

Product_id

Customer_id

Txn_id

Store_id

Junk_id

Txn_AMT

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Degenerate Dimension

The items in fact table which is neither measure nor related to

dimension is consider as Degenerate dimension

Degenerate dimension always has one attribute. Rater than

creating a separate dimension we merge this in fact table.

Degenerate dimension is a unique no which generate at time

of transaction.

Bill no can be a degenerate dimension.

Sales fact tableBill no

Prod_id

Cust_id

Time_id

Price_amount

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Star Schema

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Snowflake Schema

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Fact Constellation or Galaxy Schema

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Thanks