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Data Transformation

Data Transformation is the process of converting data from one format to another, typically from the format of a source system into the required format of a destination system. Data transformation is a component of most data integration and data management tasks, such as data wrangling and data warehousing. One step in the ELT/ETL process, data transformation may be described as either “simple” or “complex,” depending on the kinds of changes that must occur to the data before it is delivered to its target destination. The data transformation process can be automated, handled manually, or completed using a combination of the two. Today, the reality of big data means that data transformation is more important for businesses than ever before. An ever-increasing number of programs, applications, and devices continually produce massive volumes of data. And with so much disparate data streaming in from a variety of sources, data compatibility is always at risk. That’s where the data transformation process comes in: it allows companies and organizations to convert data from any source into a format that can be integrated, stored, analyzed, and ultimately mined for actionable business intelligence.[1]

  1. Defining Data Transformation Talend