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In-database computing merges database warehouses with analytical systems, allowing for faster and more accurate calculations without the need to export data. This technology is commonly used by large enterprises for predictive analytics and real-time results. Small businesses typically do not require this feature.
In-database computing, also known as in-database analytics, is a technology that focuses on merging database warehouses with analytical systems. Typically, a database warehouse needs to export information to an analytical program to perform extensive calculations on the data. With in-database processing, all calculations are performed by a single program. This saves time, as the time required for export is removed and speeds up the database to produce real-time results. Many database vendors that make database programs for large enterprises offer this feature.
Database programs that do not include in-database processing have separate database stores from analytic programs. A database warehouse is a type of database meant for storing and reporting data. These warehouses include a layer for developer raw data, a layer for user data, and a third layer where users enter data. A warehouse database can usually do some calculations, but only small ones.
Analytical programs are able to perform these larger calculations, but only if the database warehouse exports the data. For small databases, moving data between the two programs may not hinder performance, but large companies may have to wait hours for the calculations to finish. Moving data could also lead to inaccuracies if the administrator forgets to move a certain part of the database.
Processing in the database corrects these potential errors and problems. Instead of moving the data, all processing and calculations are done by the database warehouse itself. Performance benefits include a large increase in speed, enough for the database to deliver real-time results, and near removal of potential inaccuracies. Many large databases, such as those used for fraud detection and stock market databases, use this technology.
One of the key features of in-database computing is predictive analytics. This is when an analytical program takes database information and tries to predict a trend. This isn’t specific to in-database computing, but is able to make such a prediction quickly, which allows a business to do better than those with slower systems.
This type of technology is typically not needed for small businesses, so most vendors use this feature for large enterprise database programs. In-database processing is usually standard for these large enterprise solutions because it is very difficult to get results and information from the database without this processing capability. These companies also have more data than they know, and this powerful computing system is needed to look at all the data and use it to benefit the business.
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