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Saravanan Vajjiravel

Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

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Page 1: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Saravanan Vajjiravel

Page 2: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

AgendaAgenda

• Data Warehouse Overview• Cognos 8 Overview• Cognos 8 Framework Manager• Cognos 8 Report Studio• Cognos 8 Query Studio• Cognos Work Approach – Telstra• Case Study• Q/A

• Data Warehouse Overview• Cognos 8 Overview• Cognos 8 Framework Manager• Cognos 8 Report Studio• Cognos 8 Query Studio• Cognos Work Approach – Telstra• Case Study• Q/A

Page 3: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos
Page 4: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Data Warehouse

Data warehouse is a process for building decision support systems and knowledge management environment that supports both day-to-day tactical decision making and long-term business strategies. 

Bill Inmon "Subject-oriented, integrated, time variant, non-volatile collection of data in support of management's decision making process."

Data Warehouse

Data warehouse is a process for building decision support systems and knowledge management environment that supports both day-to-day tactical decision making and long-term business strategies. 

Bill Inmon "Subject-oriented, integrated, time variant, non-volatile collection of data in support of management's decision making process."

Page 5: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

DW MethodologiesDW MethodologiesTop-Down Bottom-Up

Practitioner Bill Inmon Ralph Kimball

Emphasize Data Warehouse Data Marts

Design Enterprise based normalized model; marts use a subject orient dimensional model

Dimensional model of data mart, consists star schema

Architect Multi-tier comprised of staging area and dependent data marts

Staging area and data marts

Data set DW atomic level data; marts summary data

Contains both atomic and summary data

Page 6: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Star schemasStar schemas The following figure shows a star schema with a single fact table and four

dimension tables. A star schema can have any number of dimension tables. The crow's feet at the end of the links connecting the tables indicate a many-to-one relationship between the fact table and each dimension table.

Page 7: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Snowflake schemasSnowflake schemas

The following figure shows a snowflake schema with two dimensions, each having three levels. A snowflake schema can have any number of dimensions and each dimension can have any number of levels.

Page 8: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Star vs. SnowflakeStar vs. SnowflakeSnowflake Schema Star Schema

Which Data warehouse? Good to use for small data warehouses/data marts Good for large data warehouses

Normalization(dim table) 3 Normal Form 2 Normal Demoralized Form

Ease of Use More complex queries and hence less easy to understand

Less complex queries and easy to understand

Ease of maintenance/change

No redundancy and hence more easy to maintain and change

Has redundant data and hence less easy to maintain/change

Query Performance More foreign keys-and hence more query execution time

Less no. of foreign keys and hence lesser query execution time

Page 9: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Types of FactsTypes of FactsThere are three types of facts: Additive: Additive facts are facts that can be summed up through all of the dimensions in the fact table.

The purpose of this table is to record the sales amount for each product in each store on a daily basis. Sales_Amount is the fact. In this case, Sales_Amount is an additive fact, because you can sum up this fact along any of the three dimensions present in the fact table -- date, store, and product. For example, the sum of Sales_Amount for all 7 days in a week represent the total sales amount for that week.

Semi-Additive: Semi-additive facts are facts that can be summed up for some of the dimensions in the fact table, but not the others. Non-Additive: Non-additive facts are facts that cannot be summed up for any of the dimensions present in the fact table.

The purpose of this table is to record the current balance for each account at the end of each day, as well as the profit margin for each account for each day. Current_Balance is a semi-additive fact, as it makes sense to add them up for all accounts (what's the total current balance for all accounts in the bank?), but it does not make sense to add them up through time (adding up all current balances for a given account for each day of the month does not give us any useful information). Profit_Margin is a non-additive fact, for it does not make sense to add them up for the account level or the day level.

Page 10: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Types of Fact TablesTypes of Fact Tables

Cumulative:

This type of fact table describes what has happened over a period of time. For example, this fact table may describe the total sales by product by store by day. The facts for this type of fact tables are mostly additive facts. The first example presented here is a cumulative fact table.

Snapshot:

This type of fact table describes the state of things in a particular instance of time, and usually includes more semi-additive and non-additive facts. The second example presented here is a snapshot fact table.

Page 11: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Junk Dimension / Degenerate DimensionJunk Dimension / Degenerate Dimension

Junk Dimension:

Junk dimensions are dimensions that contain miscellaneous data (like flags and indicators) that do not fit in the base dimension table.

Degenerate Dimension:

A degenerate dimension is data that is dimensional in nature but stored in a fact table. For example, if you have a dimension that only has Order Number and Order Line Number, you would have a 1:1 relationship with the Fact table. Do you want to have two tables with a billion rows or one table with a billion rows. Therefore, this would be a degenerate dimension and Order Number and Order Line Number would be stored in the Fact table.

Page 12: Saravanan Vajjiravel. Agenda Data Warehouse Overview Cognos 8 Overview Cognos 8 Framework Manager Cognos 8 Report Studio Cognos 8 Query Studio Cognos

Questions & Answers

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