The term “Data Warehouse” was first coined by Bill Inmon in 1990. According to Inmon, a data warehouse is a subject oriented, integrated, time-variant, and non-volatile collection of data. This data helps analysts to take informed decisions in an organization.
An operational database undergoes frequent changes on a daily basis on account of the transactions that take place. Suppose a business executive wants to analyze previous feedback on any data such as a product, a supplier, or any consumer data, then the executive will have no data available to analyze because the previous data has been updated due to transactions.
A data warehouses provides us generalized and consolidated data in multidimensional view. Along with generalized and consolidated view of data, a data warehouses also provides us Online Analytical Processing (OLAP) tools. These tools help us in interactive and effective analysis of data in a multidimensional space. This analysis results in data generalization and data mining.
Data mining functions such as association, clustering, classification, prediction can be integrated with OLAP operations to enhance the interactive mining of knowledge at multiple level of abstraction. That’s why data warehouse has now become an important platform for data analysis and online analytical processing.
Understanding a Data Warehouse
- A data warehouse is a database, which is kept separate from the organization’s operational database.
- There is no frequent updating done in a data warehouse.
- It possesses consolidated historical data, which helps the organization to analyze its business.
- A data warehouse helps executives to organize, understand, and use their data to take strategic decisions.
- Data warehouse systems help in the integration of diversity of application systems.
- A data warehouse system helps in consolidated historical data analysis.
Features of Data Warehouse
(i) Subject Oriented
A data warehouse is subject oriented because it provides information around a subject rather than the organization’s ongoing operations. These subjects can be product, customers, suppliers, sales, revenue, etc. A data warehouse does not focus on the ongoing operations, rather it focuses on modelling and analysis of data for decision making.
A data warehouse is constructed by integrating data from heterogeneous sources such as relational databases, flat files, etc. This integration enhances the effective analysis of data.
(iii) Time Variant
The data collected in a data warehouse is identified with a particular time period. The data in a data warehouse provides information from the historical point of view.
Non-volatile means the previous data is not erased when new data is added to it. A data warehouse is kept separate from the operational database and therefore frequent changes in operational database is not reflected in the data warehouse.
Data Warehouse Applications
As discussed before, a data warehouse helps business executives to organize, analyze, and use their data for decision making. A data warehouse serves as a sole part of a plan-execute-assess “closed-loop” feedback system for the enterprise management. Data warehouses are widely used in the following fields:
- Financial services
- Banking services
- Consumer goods
- Retail sectors
- Controlled manufacturing
Types of Data Warehouse
Information processing, analytical processing, and data mining are the three types of data warehouse applications that are discussed below:
- Information Processing: A data warehouse allows to process the data stored in it. The data can be processed by means of querying, basic statistical analysis, reporting using crosstabs, tables, charts, or graphs.
- Analytical Processing: A 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 hidden patterns and associations, constructing analytical models, performing classification and prediction. These mining results can be presented using the visualization tools.
Functions of Data Warehouse Tools and Utilities
- Data Extraction: Involves gathering data from multiple heterogeneous sources.
- Data Cleaning: Involves finding and correcting the errors in data.
- Data Transformation: Involves converting the data from legacy format to warehouse format.
- Data Loading: Involves sorting, summarizing, consolidating, checking integrity, and building indices and partitions.
- Refreshing: Involves updating from data sources to warehouse.
|Data Warehouse (OLAP)
|It involves historical processing of information.
|It involves day-to-day processing.
|OLAP systems are used by knowledge workers such as executives, managers, and analysts.
|OLTP systems are used by clerks, DBAs, or database professionals.
|It is used to analyze the business.
|It is used to run the business.
|It focuses on Information out.
|It focuses on Data in.
|It is based on Star Schema, Snowflake Schema, and Fact Constellation Schema.
|It is based on Entity Relationship Model.
|It focuses on Information out.
|It is application oriented.
|It contains historical data.
|It contains current data.
|It provides summarized and consolidated data.
|It provides primitive and highly detailed data.
|It provides summarized and multidimensional view of data.
|It provides detailed and flat relational view of data.
|The number of users is in hundreds.
|The number of users is in thousands.
|The number of records accessed is in millions.
|The number of records accessed is in tens.
|The database size is from 100GB to 100 TB.
|The database size is from 100 MB to 100 GB.
|These are highly flexible.
|It provides high performance.
Data warehousing is the process of constructing and using a data warehouse. A data warehouse is constructed by integrating data from multiple heterogeneous sources that support analytical reporting, structured and/or ad hoc queries, and decision making. Data warehousing involves data cleaning, data integration, and data consolidations.
Using Data Warehouse Information
There are decision support technologies that help utilize the data available in a data warehouse. These technologies help executives to use the warehouse quickly and effectively. They can gather data, analyze it, and take decisions based on the information present in the warehouse. The information gathered in a warehouse can be used in any of the following domains:
- Tuning Production Strategies: The product strategies can be well tuned by repositioning the products and managing the product portfolios by comparing the sales quarterly or yearly.
- Customer Analysis: Customer analysis is done by analyzing the customer’s buying preferences, buying time, budget cycles, etc.
- Operations Analysis: Data warehousing also helps in customer relationship management, and making environmental corrections. The information also allows us to analyze business operations.
Integrating Heterogeneous Databases
To integrate heterogeneous databases, we have two approaches
- Query-driven Approach
- Update-driven Approach
This is the traditional approach to integrate heterogeneous databases. This approach was used to build wrappers and integrators on top of multiple heterogeneous databases. These integrators are also known as mediators.
Process of Query-Driven Approach
(i) When a query is issued to a client side, a metadata dictionary translates the query into an appropriate form for individual heterogeneous sites involved.
(ii) Now these queries are mapped and sent to the local query processor.
(iii) The results from heterogeneous sites are integrated into a global answer set.
Disadvantage of Query-Driven Approach
- Query-driven approach needs complex integration and filtering processes.
- This approach is very inefficient.
- It is very expensive for frequent queries.
- This approach is also very expensive for queries that require aggregations.
This is an alternative to the traditional approach. Today’s data warehouse systems follow update-driven approach rather than the traditional approach discussed earlier. In update-driven approach, the information from multiple heterogeneous sources are integrated in advance and are stored in a warehouse. This information is available for direct querying and analysis.
Advantage of Update-Driven Approach
This approach has the following advantages
- This approach provide high performance
- The data is copied, processed, integrated, annotated, summarized and restructured in semantic data store in advance.
- Query processing does not require an interface to process data at local sources.