What Is Data Transformation?

Data transformation, or DT for short, can be compared to a trick performed by a magician using data. It takes data in one form and makes it disappear, then makes the data emerge in a second form, presumably an improved form. The act of modifying the data is referred to as the "transformation process." Imagine turning a nasty, sour lemon into a refreshing, delectable glass of lemonade by adding sugar and a little lemon juice. Although the lemon has transformed, in its very essence, it is still a lemon. The exact process, which involves taking data from one form and translating it into another, is used in data transformation. The primary objective of data transformation is to improve the readability and usability of the underlying data. It might require converting a PDF file to a Word document or consolidating data from numerous sources into a single database. Data transformation uses several methodologies, such as mapping, cleaning, and standardization. The mapping process entails collecting data from one source and mapping it to a new structure, such as a GPS map, which lets you find your way from one spot to another. Deleting any data that is soiled or inaccurate is known as "cleaning," and it is analogous to washing a dirty automobile to make it appear as though it were brand new. The process of normalization entails arranging the data into a more consistent format. This process is analogous to organizing a disorderly closet into a nice and organized place. Data transformation is typically used alongside data integration and warehousing. The process of data integration involves merging data from several different sources into a single database. In contrast, data warehousing consists of keeping data in a central location to be easily accessed and analyzed. These techniques, when combined, assist companies in making more effective use of the data they possess. "ETL"—Extract, Transform, and Load—is a crucial data transformation term. The process of taking data from various sources, converting it into a form that can be used, and then feeding it into a data warehouse is called ETL. ETL is commonly used in large enterprises that collect a large amount of data from various sources. Data pipelines, which transfer data, are another technical term. The data transformation process is frequently included, along with data integration and warehousing, as a component of a more comprehensive data pipeline.