Description: The normalization process in data management refers to the systematic steps taken to transform and structure data in a way that reduces redundancy and improves data integrity. This process is fundamental in handling large volumes of data, as it allows for efficient and coherent organization of information. Normalization involves dividing data into smaller tables and establishing relationships between them, which facilitates data management and analysis. Additionally, by eliminating data duplication, errors are minimized and storage is optimized. In the context of real-time data processing platforms, normalization becomes a key tool to ensure that data flows are accurate and useful for decision-making. This process not only enhances data quality but also enables organizations to gain clearer and more meaningful insights from their analyses. In summary, normalization is an essential component in data architecture, especially in massive data processing environments, where integrity and efficiency are paramount.
