Data Warehouse-overview

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.

Why a Data Warehouse is Separated from Operational Databases

A data warehouses is kept separate from operational databases due to the following reasons:

  • An operational database is constructed for well-known tasks and workloads such as searching particular records, indexing, etc. In contract, data warehouse queries are often complex and they present a general form of data.
  • Operational databases support concurrent processing of multiple transactions. Concurrency control and recovery mechanisms are required for operational databases to ensure robustness and consistency of the database.
  • An operational database query allows to read and modify operations, while an OLAP query needs only read only access of stored data.
  • An operational database maintains current data. On the other hand, a data warehouse maintains historical data.
  1. Data Warehouse Features

  2. Data Warehouse Applications

  3. Data Warehousing – Concepts

  4. Data Warehousing – Terminologies

  5. Data Warehousing – Delivery Process

  6. Data Warehousing – System Processes

  7. Data Warehousing – Architecture

  8. Data Warehousing – OLAP

  9. Data Warehousing – Relational OLAP

  10. Data Warehousing – Schemas

  11. Data Warehousing – Partitioning Strategy

  12. Data Warehousing – Metadata Concepts

  13. Data Warehousing – Data Marting

  14. Data Warehousing – System Managers

  15. Data Warehousing – Process Managers

  16. Data Warehousing – Security

  17. Data Warehousing – Backup

  18. Data Warehousing – Tuning

  19. Data Warehousing – Testing