DATA WAREHOUSE

Data Warehouse definition by William H. Inmonna:

A data warehouse is a:

·         Subject-oriented

·         Integrated

·         Time-varying

·         Non-volatile

collection of data that supports management's decision-making process.

A data warehouse is a centralized repository that stores data from multiple heterogeneous information sources and transforms them into a common, multidimensional data model for efficient querying and analysis.

The term "Data Warehouse" was first coined by Bill Inmon in 1990. He said that Data warehouse is subject Oriented, Integrated, Time-Variant and nonvolatile collection of data. This data helps in supporting decision making process by analyst in an organization.

The data warehouse is constructed by integrating the data from multiple heterogeneous sources. This data warehouse supports analytical reporting, structured and/or ad hoc queries and decision making.

Data Warehouse Tools and Utilities Functions

The following are the functions of Data Warehouse tools and Utilities:

·         Data Extraction - Data Extraction involves gathering the data from multiple heterogeneous sources.

·         Data Cleaning - Data Cleaning involves finding and correcting the errors in data.

·         Data Transformation - Data Transformation involves converting data from legacy format to warehouse format.

·         Data Loading - Data Loading involves sorting, summarizing, consolidating, checking integrity and building indices and partitions.

·         Refreshing - Refreshing involves updating from data sources to warehouse.

Note: Data Cleaning and Data Transformation are important steps in improving the quality of data and data mining results.

Data Warehouse Features

The key features of Data Warehouse such as Subject Oriented, Integrated, Nonvolatile and Time-Variant are discussed below:

·         Subject Oriented - The Data Warehouse is Subject Oriented because it provides us the information around a subject rather the organization's ongoing operations. These subjects can be product, customers, suppliers, sales, revenue etc. The data warehouse does not focus on the ongoing operations rather it focuses on modelling and analysis of data for decision making.

·         Integrated - Data Warehouse is constructed by integration of data from heterogeneous sources such as relational databases, flat files etc. This integration enhances the effective analysis of data.

·         Time-Variant - The Data in Data Warehouse is identified with a particular time period. The data in data warehouse provide information from historical point of view.

·         Non Volatile - Nonvolatile means that the previous data is not removed when new data is added to it. The data warehouse is kept separate from the operational database therefore frequent changes in operational database are not reflected in data warehouse.

Note: - Data Warehouse does not require transaction processing, recovery and concurrency control because it is physically stored separate from the operational database.

Data Warehouse Applications

As discussed before Data Warehouse helps the business executives in organize, analyze and use their data for decision making. Data Warehouse serves as a soul part of a plan-execute-assess "closed-loop" feedback system for enterprise management. Data Warehouse is widely used in the following fields:

1.     financial services

2.     Banking Services

3.     Consumer goods

4.     Retail sectors.

5.     Controlled manufacturing

The operational database undergoes the per day transactions which causes the frequent changes to the data on daily basis. But if in future the business executive wants to analyze the previous feedback on any data such as product, supplier, or the consumer data. In this case the analyst will be having no data available to analyze because the previous data is updated due to transactions.

The Data Warehouses provide us generalized and consolidated data in multidimensional view. Along with generalize and consolidated view of data the Data Warehouses also provide us Online Analytical Processing (OLAP) tools. These tools help us in interactive and effective analysis of data in multidimensional space. This analysis results in data generalization and data mining.

The data mining functions like association, clustering, classification, prediction can be integrated with OLAP operations to enhance interactive mining of knowledge at multiple level of abstraction. That's why data warehouse has now become important platform for data analysis and online analytical processing.

Understanding Data Warehouse

·         The Data Warehouse is that database which is kept separate from the organization's operational database.

·         There is no frequent updating done in data warehouse.

·         Data warehouse possess consolidated historical data which help the organization to analyze its business.

·         Data warehouse helps the executives to organize, understand and use their data to take strategic decision.

·         Data warehouse systems available, helps in integration of diverse application systems.

·         The Data warehouse system allows analysis of consolidated historical data analysis.

Why Data Warehouse is separated from Operational Databases

The following are the reasons why Data Warehouse is kept separate from operational databases:

·         The operational database is constructed for well-known tasks and workload such as searching particular records, indexing etc. but the data warehouse queries are often complex and it presents the general form of data.

·         Operational database supports the concurrent processing of multiple transactions. Concurrency control and recovery mechanism are required for operational databases to ensure robustness and consistency of database.

·         Operational database query allow to read and modify while the OLAP query need only read only access of stored data.

·         Operational database maintain the current data on the other hand data warehouse maintain the historical data.

Data Warehouse Application Types

Information processing, Analytical processing and Data Mining are the three types of data warehouse applications that are discussed below:

·         Information processing - Data Warehouse allow us to process the information stored in it. The information can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.

·         Analytical Processing - Data Warehouse supports analytical processing of the information stored in it. The data can be analyzed by means of basic OLAP operations, including slice-and-dice, drill down, drill up, and pivoting.

·         Data Mining - Data Mining supports knowledge discovery by finding the hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using the visualization tools.

S.No.	Property	OLTP	OLAP
1	User	Clerk, IT Professional	Knowledge worker
2	Function	Day to Day Operations	Decision Support
3	DB Design	Application-oriented(E-R based)	Subject-Oriented(Star, SnowFlake)
4	Data	Current, Isolated	Historical, Consolidated
5	View	Detailed, Flat Relational	Summarized, Multi-dimensional
6	Usage	Structured, Repetitive	Ad-Hoc
7	Unit of Work	Short, Simple transaction	Complex Query
8	Access	Read/write	Read Mostly
9	Operations	Index/Hash on primary key	Lots of Scans
10	#Records Accessed	Tens	Millions
11	# Users	Thousands	Hundreds
12	Data Base Size	100 MB-GB	100GB-TB
13	Metric	Trans. throughput	Query throughput, response