What is a data warehouse?
A data warehouse is a collection of data marts representing historical data from different operations in the company. This data is stored in a structure optimized for querying and data analysis as a data warehouse. Table design, dimensions and organization should be consistent throughout a data warehouse so that reports or queries across the data warehouse are consistent. A data warehouse can also be viewed as a database for historical data from different functions within a company.
What is a data mart?
A data mart is a segment of a data warehouse that can provide data for reporting and analysis on a section, unit, department or operation in the company, e.g. sales, payroll, production. Data marts are sometimes complete individual data warehouses which are usually smaller than the corporate data warehouse.
What are the benefits of data warehousing?
Data warehouses are designed to perform well with aggregate queries running on large amounts of data.
The structure of data warehouses is easier for end users to navigate, understand and query against unlike the relational databases primarily designed to handle lots of transactions.
Data warehouses enable queries that cut across different segments of a company's operation. E.g. production data could be compared against inventory data even if they were originally stored in different databases with different structures.
Queries that would be complex in much normalized databases could be easier to build and maintain in data warehouses, decreasing the workload on transaction systems.
Data warehousing is an efficient way to manage and report on data that is from a variety of sources, non uniform and scattered throughout a company.
Data warehousing is an efficient way to manage demand for lots of information from lots of users.
Data warehousing provides the capability to analyze large amounts of historical data for nuggets of wisdom that can provide an organization with competitive advantage.
What is OLAP?
OLAP stands for Online Analytical Processing.
It uses database tables (fact and dimension tables) to enable multidimensional viewing, analysis and querying of large amounts of data. E.g. OLAP technology could provide management with fast answers to complex queries on their operational data or enable them to analyze their company's historical data for trends and patterns.
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