Data Warehousing
What is data warehousing?
Data warehousing is a technology that sums up structured data from one or more sources so it could be compared and analyzed for greater business intelligence. It 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.
Why do you need to know about data warehousing?
A data warehouse is used to correlate broad business data to provide smoother execution insight into corporate performance.
Why data warehouse is used?
Many types of business data are analyzed via data warehoused. The need for a data warehouse becomes necessary when analytic requirements run afoul of the ongoing performance of operational databases. Data warehouses typically store historical data by integrating copies of transaction data from disparate sources. Data warehouses can also use real-time data feeds for reports that use the most current, integrated information. Running a complex query on a database requires the database to enter a temporarily fixed state. This is often untenable for transactional databases. A data warehouse is employed to do the analytic work, leaving the transactional database free to focus on transactions.
The other benefits of a data warehouse are the ability to analyze data from multiple sources and to negotiate differences in storage schema using the ETL Process (Extract, Transform, and Load Process).
Besides these, there are few disadvantages of data warehousing. A data warehouse is expensive to scale and does not excel at handling raw, unstructured, or complex data. However, a data warehouse is still an important tool in the era of big data.