Data Warehousing and Mining Lecture 4
Difference B/w Operational Systems & Information System
| Operational | Informational | |
|---|---|---|
| Data Context | Current Values | Archived Derived Summarized |
| Data Structure | Optimized for Transactions | Optimized for complex Queries |
| Access Frequency | High | Medium to Low |
| Access Type | Read Update Delete | Read |
| Usage | Predictable | Ad Hoc, Random |
| Response Times | Sub Seconds | Several Seconds to minutes |
| Users | Large Numbers | Relatively Small Numbers |
Database V/S Data Warehouse
DataBase is an instant of a current data and data warehouse is a history and analysis on the previous data. With this information it is obvious That a database is used more frequently than a Data Warehouse. You can use this analogy to remember this : A user will deposit and withdraw money more frequently, However a user will not analyse data such as their savings record or credit score as frequent as a deposit.
Properties of Datawarehouse
Data warehouses are subject oriented, Time Invariant and Accessible.
- Subject Oriented :
- Data Warehouse is not a replacement to DBMS it is a layer above it. That means its use is subjective.
- Integrated Data:
- Remove Inconsistencies
- Standardize the various data elements
- Make sure of the meanings of data names in each source applications
- Standardization is necessary so the data warehouse can be valid
- Naming Conventions
- Codes
- Data Attributes
- Measurements
- File Naming Conventions is a good example of standardization.
- A file naming convention enables a person to have data sorted already. For example a teacher who names their student’s project files with a naming convention has their files sorted and it becomes easy to grade the students. However, Once a naming convention is followed it cannot be defied.
- Time Variant Data
- Allows for analysis of the past
- Relates information to the present
- Enables forecast
OLTP
- Online Transaction processing Refers to the Operational System Handling Data Base Management System.
OLAP
- Online Analytical Processing Refers to the Informational System Handling Data Warehouse and Data Mining Operations.
Data Granularity
The term Data Granularity refers to the level of detail that a data has. A data can have multiple level of details. Data Warehouse have at least two levels of granularity. They are as follows
- Coarse Level
- Fine Level
Scenario 1
ABC Pvt. Ltd is a company with branches at Mumbai, Delhi, Chennai and Bangalore. The Sales Manager quarterly Sales Report.
Each Branch has a Separate Operational System.
Scenario 3
Cakes & Cookies is a small new company Chairman needs analysis and next steps.
- Improve quality of data
- Then Analyse it then chart it and show it to chairman where he thinks of next better steps
- Use query analysis tool to support adhoc queries.
ETL
Extraction Transformation and Loading The conversion of Ad hoc data in operational systems needs to be Processed and transformed before transferring it into a data warehouse system.
References
Information
- date: 2025.01.10
- time: 10:20