Introduction

In the “transform” stage of ETL, raw data is processed and transformed into a usable form. Data is cleaned, prepared, and transformed according to specific business contexts and the requirements of the target system. In this step, we add business value to otherwise raw and unusable data. Therefore, the transform stage is crucial in ETL pipelines because it ensures that data is of high quality and consistency. Data might not be usable without proper data preparation and transformation, leading to incorrect results and decisions.

Types of transformations

There are various types of transformations that can be applied in the transform stage of ETL pipelines. Here are some examples:

• Data structuring & typing: It casts data to an appropriate data type or data format.

• Anonymizing & encrypting: It ensures data privacy and security.

• Data cleaning: It helps remove duplicate records and fill in missing values.

• Data normalization: It converts data to common units.

• Filtering, sorting, aggregating, & binning: These are general data processing techniques.

• Joining/merging data: It combines data from multiple data structures.