Data Warehousing

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Data Warehousing by Mind Map: Data Warehousing

1. Allows more appropriate technology for queries and reports.

2. Inmon the father of Data Warehouse defined Data warehouse as :"A Warehouse is a subjectoriented, integrated, time-variant and non-volatile collection of data in support of management's decision making process”

3. Defination:-A centralized repository of all data generated by all departments and units of a large organization is called Data warehouse.

4. Uses Of Data Warehousing

4.1. Data warehouse can be used for number of activities including traditional analysis , information visualization .

4.2. Business Analysis : For decision Making.

4.3. Forecasting :You can use historical profitability figures to estimate future revenue.

5. Data Warehouse Architecture

5.1. 1.Staging area:Staging area of the data warehouse is both a storage area and a set of processes commonly presentation area.

5.2. 2.Load Manager:

5.3. 2.2:Also called the front end component, it performance all the operations associated with the extraction and

5.4. 2.3:loading of data into the warehouse.

5.5. 2.4:These operations include simple transformations of the data to prepare the data for entry into the warehouse.

5.6. 3.Warehouse Manager:

5.7. 3.1:Performs all the operations associated with the management of the data in the warehouse.

5.8. 3.2:The operations performed by this component include analysis of data to ensure consistency,

5.9. 3.3:Transformation and merging of source data, creation of indexes and views, aggregations, and backing-up data.

5.10. 4.Query Manager:

5.11. 4.1:Also called backend component, it performs all the operations associated with the management of user queries.

5.12. 4.2:The operations performed by this component include directing queries to the appropriate tables and scheduling

5.13. 4.3:The execution of queries.

5.14. 5.ETL

6. Advantages Of Data Warehousing

6.1. Provides a more simple query interface to users .

6.2. Easy way of reporting across multiple systems

7. Data Preprocessing techniques

7.1. 1.Data Cleaning

7.2. 2.Data Integration

7.3. 3.Data Transformation

7.4. 4.Data Reduction

8. Characteristics of Data Warehousing:

8.1. 1.

8.1.1. Subject Oriented

8.1.2. 2. Integrated 3.Time-variant 4. Non-volatile

9. Data Cube Operations

9.1. 1.Slicing

9.2. 2.Dicing

9.3. 3.

9.3.1. Rotating./Pivoting

9.3.2. 4. Roll-up 5.Drill up

10. Data Mining: Data mining automates the process of locating and extracting the hidden patterns and knowledge

11. On-Line Analytical Processing (OLAP) :

11.1. OLAP is the use of a set of graphical tools that provides users with multidimensional views of their data and allows them to analyze the data using simple windowing techniques.

11.2. OLAP provides quick response to the user queries irrespective of the database size & its complexity.

11.3. It helps the manager to compare the various dimensions & can have customized report generation

11.4. An OLAP database does not need to be as large as a data warehouse, since not all transactional data is needed for trend analysis. Using Open Database Connectivity (ODBC), data can be imported from existing relational databases to create a multidimensional database for OLAP.

12. OLTP (On-line Transaction:

12.1. It is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases

13. Types of OLAP Servers :

13.1. MOLAP(Multidimensional Online Analytical processing)

13.2. ROLAP(Relational Online Analytical Processing)

13.3. HOLAP(Hybrid Online Analytical Processing)

14. Ware House Schema :The schema is a logical description of the entire database

14.1. 1.

14.1.1. Star schema

14.1.2. 2. Snowflake schema 3. Fact constellation