S8-DW OLAP BI
Content
Data Warehousing
Chapter 11
1
Evolution of Data Warehouse
To offer more efficient and cost-effective services to the customer By automating business processes Resulted in accumulation of growing amounts of data in operational databases. What is database? Why database? What is its main objective?
Chapter 11 2
Database: Characteristics
Automatic Optimized Updated Quick Accurate Atomicity Consistency Integrity Durable
Chapter 11 3
Database for Transaction Process System: OLTPs
ODS: Operational Data Stores • Database for TPS. • Benefits to operational portions of business. It provides detail data. It is optimized for frequent access It provides faster response times.
Chapter 11
4
Is there any problem in Databases/ Operational Systems?
Chapter 11
5
Problems in Database/ Operational Systems
Does Not support Decision-making History is lost Time consuming, Duplications Design Garbage, Disparate systems Multi-dimensional view not possible Not for entire organization Operational systems with overlapping and sometimes contradictory definitions, inconsistent
Chapter 11
6
Question
How to use operational data (ODS) to support decision-making, as a means of gaining competitive advantage?
Chapter 11
7
The Data Warehouse
A Data Warehouse is a repository of * Subject-Oriented * Historical data * Easily accessible: Access Tools * Ready for Analytical Processing * Exclusively for Decision-Making activities * For the Entire Enterprise
Chapter 11
8
Data Warehouse: What it Stores?
Organized around major subjects (decisionsupport data) of the enterprise (e.g. customers, products, sales) rather than major application areas (application-oriented data; of the enterprise (e.g. customer invoicing, stock control, product sales). The integrated data source must be made consistent to present a unified view of the data to the users.
Chapter 11
9
The Data Warehouse Continued
Characteristics: A Series of Snapshots
Snapshot: Data is only accurate and valid at some point in time or over some time interval. Time variant. Stores past data Nonvolatile. Not updated in real-time Relational. Starflake/ Snowflake Schema Client/server. Providing end user an easy access to its data. Web-based. Support for Web-based applications
Chapter 11 10
The Data Warehouse Continued
Chapter 11
11
The Data Mart
A data mart ** small scaled-down version of a DW ** designed for a department or SBU ** Contain less information compare to DW ** Response time better than DW ** Easier accessibility than DW.
Chapter 11
12
The Data Cube
Multidimensional databases (sometimes called OLAP) ** Data in these databases: Cubes ** Data Cubes: Preprocessed Query ** Organize facts by dimensions, such as geographical region, product line, salesperson, time.
Example-1: Quantities of a product sold by *specific retail locations during *certain time periods by *salesperson. Example-2: Sales volume by *department, by *day, by *month, by *year for a *specific region Cubes provide faster: Queries, Slices and Dices of the
information, Rollups, Drill Downs
Chapter 11
13
Business Intelligence
Business intelligence (BI): A broad category of applications and techniques For gathering, storing, analyzing and providing access to data. Better business and strategic decisions. Major applications include the activities of query and reporting, OLAP, DSS, data mining, forecasting and statistical analysis.
Starts with Knowledge Discovery
Chapter 11 14
Business Intelligence Continued
How It Works.
Chapter 11 15
Comparison of OLTP Systems and Data Warehousing
Chapter 11
16
Data Warehouse Queries
End-user access tools include:
Reporting, query, and application development tools Executive information systems (EIS) OLAP tools Data mining tools The above tools can be categorised on the basis of the capability of handling simple to complex queries.
Chapter 11
17
Examples of Typical DW Queries
Simple Queries Complex Queries
Chapter 11
18
Problems of Data Warehousing
Underestimation of resources for data loading Hidden problems with source systems Required data not captured Increased end-user demands Data homogenization
Chapter 11 19
Problems of Data Warehousing
High demand for resources Data ownership High maintenance Long duration projects Complexity of integration
Chapter 11 20
Chapter 11
1
Evolution of Data Warehouse
To offer more efficient and cost-effective services to the customer By automating business processes Resulted in accumulation of growing amounts of data in operational databases. What is database? Why database? What is its main objective?
Chapter 11 2
Database: Characteristics
Automatic Optimized Updated Quick Accurate Atomicity Consistency Integrity Durable
Chapter 11 3
Database for Transaction Process System: OLTPs
ODS: Operational Data Stores • Database for TPS. • Benefits to operational portions of business. It provides detail data. It is optimized for frequent access It provides faster response times.
Chapter 11
4
Is there any problem in Databases/ Operational Systems?
Chapter 11
5
Problems in Database/ Operational Systems
Does Not support Decision-making History is lost Time consuming, Duplications Design Garbage, Disparate systems Multi-dimensional view not possible Not for entire organization Operational systems with overlapping and sometimes contradictory definitions, inconsistent
Chapter 11
6
Question
How to use operational data (ODS) to support decision-making, as a means of gaining competitive advantage?
Chapter 11
7
The Data Warehouse
A Data Warehouse is a repository of * Subject-Oriented * Historical data * Easily accessible: Access Tools * Ready for Analytical Processing * Exclusively for Decision-Making activities * For the Entire Enterprise
Chapter 11
8
Data Warehouse: What it Stores?
Organized around major subjects (decisionsupport data) of the enterprise (e.g. customers, products, sales) rather than major application areas (application-oriented data; of the enterprise (e.g. customer invoicing, stock control, product sales). The integrated data source must be made consistent to present a unified view of the data to the users.
Chapter 11
9
The Data Warehouse Continued
Characteristics: A Series of Snapshots
Snapshot: Data is only accurate and valid at some point in time or over some time interval. Time variant. Stores past data Nonvolatile. Not updated in real-time Relational. Starflake/ Snowflake Schema Client/server. Providing end user an easy access to its data. Web-based. Support for Web-based applications
Chapter 11 10
The Data Warehouse Continued
Chapter 11
11
The Data Mart
A data mart ** small scaled-down version of a DW ** designed for a department or SBU ** Contain less information compare to DW ** Response time better than DW ** Easier accessibility than DW.
Chapter 11
12
The Data Cube
Multidimensional databases (sometimes called OLAP) ** Data in these databases: Cubes ** Data Cubes: Preprocessed Query ** Organize facts by dimensions, such as geographical region, product line, salesperson, time.
Example-1: Quantities of a product sold by *specific retail locations during *certain time periods by *salesperson. Example-2: Sales volume by *department, by *day, by *month, by *year for a *specific region Cubes provide faster: Queries, Slices and Dices of the
information, Rollups, Drill Downs
Chapter 11
13
Business Intelligence
Business intelligence (BI): A broad category of applications and techniques For gathering, storing, analyzing and providing access to data. Better business and strategic decisions. Major applications include the activities of query and reporting, OLAP, DSS, data mining, forecasting and statistical analysis.
Starts with Knowledge Discovery
Chapter 11 14
Business Intelligence Continued
How It Works.
Chapter 11 15
Comparison of OLTP Systems and Data Warehousing
Chapter 11
16
Data Warehouse Queries
End-user access tools include:
Reporting, query, and application development tools Executive information systems (EIS) OLAP tools Data mining tools The above tools can be categorised on the basis of the capability of handling simple to complex queries.
Chapter 11
17
Examples of Typical DW Queries
Simple Queries Complex Queries
Chapter 11
18
Problems of Data Warehousing
Underestimation of resources for data loading Hidden problems with source systems Required data not captured Increased end-user demands Data homogenization
Chapter 11 19
Problems of Data Warehousing
High demand for resources Data ownership High maintenance Long duration projects Complexity of integration
Chapter 11 20
