1. An Overview of Data Warehousing and OLAP Technology

2. Outline Introduction Architecture of data warehousing Back end tools
OLTP applications vs. OLAP applications
Architecture of data warehousing
Back end tools
Conceptual model
Front end tools
Some examples

3. Introduction (OLTP applications)
OLTP applications typically automate clerical data processing tasks.
such as order entry and banking transactions
These tasks are structured and repetitive, and consist of short, atomic, isolated transactions.
The transactions require detailed, up-to-date data.
Consistency and recoverability of the operational database are critical.

4. Introduction (OLAP applications)
OLAP applications are designed for supporting decision making.
Historical, summarized and consolidated data are often used.
The workload of data warehouses are query intensive with mostly ad hoc, complex queries that access millions of records.
Query throughput and response times are critical.

5. Introduction (problem statement)
Given that operational databases, trying to execute complex OLAP queries would result in unacceptable performance.
Decision support requires data that might be missing from the operational databases.
Decision support requires consolidating data from many heterogeneous sources.
Supporting the multidimensional data models and operations requires special data organization, access methods and implementation methods.
=>Data Warehouse

6. Architecture of data warehouse

7. Back end tools Data extraction Data clearing Load Refresh
ODBC, Oracle Open Connect, and Sybase Enterprise Connect, etc.
Data clearing
Tools that help to detect data anomalies and correct them.
Load
Tools that help to load data into the warehouse.
Refresh
Refreshing a warehouse consists in propagating updates on source data.

8. Conceptual model

9. Database design
MOLAP
Array
ROLAP
Star schema

10. Warehouse servers Index structures Materialized views
Bit map indices, join indices
Materialized views
Transformation of complex SQL queries
Parallel processing
SQL extensions
Aggregate function
Reporting features
Multiple group-by
Comparisions

11. Front end tools Spreadsheet Query/Reporting Data mining tools
Operations for spreadsheet
Pivoting
Rollup
Drill-down
Slice_and_dice
Query/Reporting
Data mining tools

12. Examples
Oracle
Informix
Sybase …