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Step one: Data interpretation. The first step in data transformation is interpreting your data to determine which type of data you currently have, and what you need to transform it into. Step two: Pre-translation data quality check. Step three: Data translation. Step four: Post-translation data quality check. Conclusion.
Data transformation is the mapping and conversion of data from one format to another. For example, XML data can be transformed from XML data valid to one XML Schema to another XML document valid to a different XML Schema. Other examples include the data transformation from non-XML data to XML data.
In computing, Data transformation is the process of converting data from one format or structure into another format or structure.
Other common reasons to transform data include: You are moving your data to a new data store. For example, you are moving to a cloud data warehouse, and you need to change the data types. You want to join unstructured data or streaming data with structured data, so you can analyze the data together.
Data is transformed to make it better-organized. Transformed data may be easier for both humans and computers to use. Properly formatted and validated data improves data quality and protects applications from potential landmines such as null values, unexpected duplicates, incorrect indexing, and incompatible formats.
Data transformation defined Data transformation is the process of converting data from one format or structure into another format or structure. Data transformation is critical to activities such as data integration and data management. Perform data discovery where you identify the sources and data types.
Transforms are usually applied so that the data appear to more closely meet the assumptions of a statistical inference procedure that is to be applied, or to improve the interpretability or appearance of graphs. Nearly always, the function that is used to transform the data is invertible, and generally is continuous.
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