Data Transformation
Data transformation is the process of converting data from one format, structure, or representation into another to make it suitable for analysis, storage, or integration. It involves operations like cleaning, filtering, aggregating, and restructuring data to meet specific requirements. This is a fundamental step in data processing pipelines, enabling data to be used effectively in applications, reports, or machine learning models.
Developers should learn data transformation to handle real-world data that is often messy, inconsistent, or in incompatible formats, such as when integrating data from multiple sources like APIs, databases, or files. It is essential for tasks like data warehousing, ETL (Extract, Transform, Load) processes, and preparing datasets for analytics or AI applications, ensuring data quality and usability.