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SELECT list is the list of columns or SQL expressions to be returned by the query. This is approximately the relational algebra projection operation. AS optionally provides an alias for each column or expression in the SELECT list. This is the relational algebra rename operation. FROM specifies from which table to get the data. [3]
Extract, transform, load (ETL) is a three-phase computing process where data is extracted from an input source, transformed (including cleaning), and loaded into an output data container. The data can be collected from one or more sources and it can also be output to one or more destinations.
A pivot table field list is provided to the user which lists all the column headers present in the data. For instance, if a table represents sales data of a company, it might include Date of sale, Sales person, Item sold, Color of item, Units sold, Per unit price, and Total price. This makes the data more readily accessible.
The base data and the dimension tables are stored as relational tables and new tables are created to hold the aggregated information. It depends on a specialized schema design. This methodology relies on manipulating the data stored in the relational database to give the appearance of traditional OLAP's slicing and dicing functionality.
Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...
Data cleansing or data cleaning is the process of identifying and correcting (or removing) corrupt, inaccurate, or irrelevant records from a dataset, table, or database. It involves detecting incomplete, incorrect, or inaccurate parts of the data and then replacing, modifying, or deleting the affected data. [ 1 ]
Planning data. This includes all the restraints and directions to produce such items as: routing, labor and machine standards, quality and testing standards, pull/work cell and push commands, lot sizing techniques (i.e. fixed lot size, lot-for-lot, economic order quantity), scrap percentages, and other inputs.
In other cases data might be brought into the staging area to be processed at different times; or the staging area may be used to push data to multiple target systems. As an example, daily operational data might be pushed to an operational data store (ODS) while the same data may be sent in a monthly aggregated form to a data warehouse.