Structured Query Language, SQL, is vital when it comes to mining and analysis of data. It is a standard language that is used to manipulate relational databases so as to obtain specific information. Using SQL, one can not only obtain information through a query statement but also insert, update and modify data from a relational database. The SQL was developed in the early 1970s by Raymond Boyce and Donald Chamberlin was eventually released in 1979 by Relational Software Inc. (Pirvali, 2010). Over the years, the SQL has been modified and standardized to meet certain criteria set by American National Standards Institute that enable them to be vendor-compliant.
Learning SQL can be challenging as it is vast and complicated. It involves writing statements or SQL scripts that relays instruction to the relational database. Initially, SQL statements used to be executed without Graphical User Interfaces (GUIs), however, over the years, the GUIs have been integrated into relational databases to make database management more efficient. Moreover, graphical tools have increasingly been utilized to have the same functionality as certain queries so as to make the database manipulation easier. SQL code is divided into different categories that include; Queries, Data Manipulation Language (DML), Data Definition Language (DDL) and Data Control language (DCL) (Pirvali, 2010). Queries are operationalized by the SELECT statement which selects the particular set of data that one intends to manipulate. Data Manipulation Language, DML, on the other hand, is used to modify the data the through adding data using the ADD function, updating the data in the database using the UPDATE function, deleting data using the DELETE function as well as several other control statements. Data Definition Language (DDL) plays a great role in managing tables and index structures. DDL includes statements such as CREATE, ALTER, TRUNCATE and DROP. Finally, the Data Control Language (DCL), manages rights and permissions so that different users can only access the sections of the database that are pertinent to their work. Statements in this category include GRANT and REVOKE (Pirvali, 2010).
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Organizations all over the world usually have to manage a lot of data that they get from their customers, patients as well as other stakeholders with different periodicity. For this data to be useful, it has to be processed and analyzed in order to for patterns to be identified, different business phenomena to be explained and lastly for predictions to be made (Fotache & Strimbei, 2015). These big volumes of data utilize SQL queries so that information can be extracted from the datasets and decisions can be made from the analysis. SQL features greatly in statistical analysis through different packages such as SPSS, SAS, Stata and Minitab (Fotache & Strimbei, 2015). These packages are designed to easily perform a range of statistical analysis on several datasets. SQL statements are integrated into these softwares which provide graphical user interfaces (GUIs) and allow for statistics of data in a dataset to obtained just from a few key strokes. Therefore, SQL is integral in data mining as it is used to fetch data from a database using certain statements. Moreover, through various SQL statements statistical analysis can be easily performed even on extremely large datasets.
Screenshot of the Northwind Database tables
References
Pirvali, B., & Blanco, F. (2010). U.S. Patent Application No. 12/188,915.
Smedley, R. R., Laroche, G. R., & Clapper, M. R. (2001). U.S. Patent No. 6,253,200 . Washington, DC: U.S. Patent and Trademark Office.
Fotache, M., & Strimbei, C. (2015). SQL and data analysis. Some implications for data analysits and higher education. Procedia Economics and Finance , 20 , 243-251.
