Ralph Kimball the co-founder of the data warehousing concept has defined the data warehouse as a “"a copy of transaction data specifically structured for query and analysis”.
Both definitions highlight specific features of the data warehouse. The former definition focuses on the structure and organization of the data and the latter focuses upon the usage of the data. However, a listing of the features of a data warehouse would necessarily include the aspects highlighted in both these definitions.
Data warehouse’ and ‘OLAP’ are terms which are often used interchangeably. Actually they refer to two different components of a decision support system. While data in a data warehouse is composed of the historical data of the organization stored for end user analysis, OLAP is a technology that enables a data warehouse to be used effectively for online analysis using complex analytical queries. The differences between OLAP and data warehouse is tabulated below for ease of understanding:
Data Warehouse
Data from different data sources is stored in a relational database for end use analysis
Data from different data sources is stored in a relational database for end use analysis Data is organized in summarized, aggregated, subject oriented, non volatile patterns.
Data is a data warehouse is consolidated, flexible collection of data Supports analysis of data but does not support online analysis of data.
Online Analytical Processing
A tool to evaluate and analyze the data in the data warehouse using analytical queries.
A tool which helps organize data in the data warehouse using multidimensional models of data aggregation and summarization.
Supports the data analyst in real time and enables online analysis of data with speed and flexibility.
What is the difference between Data Warehouse and online Analytical Processing?
Ralph Kimball the co-founder of the data warehousing concept has defined the data warehouse as a “"a copy of transaction data specifically structured for query and analysis”.
Both definitions highlight specific features of the data warehouse. The former definition focuses on the structure and organization of the data and the latter focuses upon the usage of the data. However, a listing of the features of a data warehouse would necessarily include the aspects highlighted in both these definitions.
Data warehouse’ and ‘OLAP’ are terms which are often used interchangeably. Actually they refer to two different components of a decision support system. While data in a data warehouse is composed of the historical data of the organization stored for end user analysis, OLAP is a technology that enables a data warehouse to be used effectively for online analysis using complex analytical queries. The differences between OLAP and data warehouse is tabulated below for ease of understanding:
Data Warehouse
Data from different data sources is stored in a relational database for end use analysis
Data from different data sources is stored in a relational database for end use analysis Data is organized in summarized, aggregated, subject oriented, non volatile patterns.
Data is a data warehouse is consolidated, flexible collection of data Supports analysis of data but does not support online analysis of data.
Online Analytical Processing
A tool to evaluate and analyze the data in the data warehouse using analytical queries.
A tool which helps organize data in the data warehouse using multidimensional models of data aggregation and summarization.
Supports the data analyst in real time and enables online analysis of data with speed and flexibility.
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