Summary
The data cleansing and transformation steps within a data pipeline are fundamental processes that are central to preparing high-quality output datasets. Creating a systematic approach to identifying and rectifying inconsistencies, inaccuracies, and missing values enhances data integrity and reliability while refining and tailoring the data to match the specific needs of your end user. Your output data can then be confidently used for any data-driven decision-making, analysis, and machine learning.
As data continues to grow in size and complexity, mastering data cleansing and transformation techniques becomes increasingly crucial, enabling data-driven organizations to uncover hidden insights and streamline operations. It is a ubiquitous and valuable skill in today’s data-dependent world.
In the next chapter, we will discuss how to load transformed data into tables.
