Describe the concept of data transformation in the context of big data engineering.

In the context of big data engineering, data transformation refers to the process of converting and reshaping raw data into a more structured and usable format for further analysis and processing. It involves applying a series of operations, such as filtering, aggregating, cleaning, normalizing, and enriching data, to make it more meaningful and valuable. Data transformation is a crucial step in the big data lifecycle because raw data often comes in various formats, structures, and sources, making it challenging to work with directly. By transforming the data, it becomes standardized, consistent, and aligned with the desired format and structure, enabling easier integration, analysis, and utilization. There are several key aspects and techniques involved in data transformation: 1. Data Cleaning: This involves removing or correcting inaccuracies, inconsistencies, and errors present in the data. It includes tasks like handling missing values, correcting typos, and resolving duplicate entries. Data cleaning ensures data quality and reliabi....