Description: Incremental loading is a data loading method where only new or modified data is transferred and updated in a storage system. This approach is fundamental in data engineering as it optimizes resource use and processing time by avoiding data duplication and unnecessary loading of information already present in the system. Unlike full loading, where all data is transferred regardless of its state, incremental loading focuses on efficiency and agility, resulting in a lower impact on system performance. This method is especially relevant in Big Data and data storage environments, where the amount of information can be overwhelming. Incremental loading can be implemented across various platforms and systems, and is a key component in ETL (Extract, Transform, Load) processes and DataOps, where speed and accuracy in data management are essential. Additionally, this approach is used in NoSQL databases and Data Lakes, where flexibility and scalability are crucial for handling large volumes of constantly changing data.
