What is Data Transformation?

Data Transformation is the method of changing data from one order or structure into another order or arrangement. Data Transformation is crucial to actions such as data unification and data administration. Data Transformation can cover a range of activities. In Data Transformation, we work on two types of methods.

In the first process, we implement data discovery, where we recognize the origins and data types. Then we determine the composition and Data Transformations that need to happen. After this, we complete data mapping to determine how particular field is mapped, transformed, merged, separated, and aggregated.

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In the second method, we pluck data from the source. The scope of sources can differ, including structured sources, like databases, or streaming sources then we do transformations. You transform the data, such as changing date formats, updating text strings or combining rows and columns, then we transfer the data to the destination store. The destination might be a database or a data warehouse that controls structured and unstructured data.

Why do we need Data Transformation?

Usually, corporations transform the data to make it cooperative with other data, transfer it to different systems, combine it with other data.

For example, if there is a parent company that wants the data of all the employees of the sales department of the child company in its database then first the data of the employees of the sales department will be extracted and then loaded to the parent company’s database.

Several reasons tell why the Data Transformation is done:

  • You want to compare sales data from another source or calculating sales from different regions.
  • You want to combine unstructured data or streaming data with structured data to examine it simultaneously.
  • You want to append information to your data to improve it.
  • You are relocating your data to a new source.

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Data Transformation using ETL - A Comprehensive Guide
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