What is Data Transformation?

Data transformation is the process of converting data from one format or structure into another format or structure. This can be done for various reasons, such as preparing data for analysis, cleaning and formatting, or making data compatible with a particular system or application.

There are many different techniques and tools, Anatics as an example,  for data transformation, depending on the specific needs and requirements of the data and the desired output. Some common methods of data transformation include:

1. Extracting and selecting relevant data from larger datasets
2. Changing the format or structure of the data, such as from unstructured to structured or vice versa
3. Aggregating or summarizing data to create new derived variables or to reduce the size of the dataset
4. Cleaning and formatting data, such as removing duplicates, correcting errors, or filling in missing values
5. Normalizing or scaling data to ensure that all variables are on the same scale
6. Encoding categorical variables as numerical values
7. Transforming data using functions or algorithms, such as taking the logarithm of a variable or applying a moving average