Description: The batch processing framework is a fundamental tool in data engineering that allows for the efficient and scalable handling of large volumes of data. This framework provides a set of tools and libraries that facilitate the execution of data processing tasks in blocks or batches, rather than processing data in real-time. This is particularly useful in situations where data is generated continuously, but it is not necessary or practical to process it immediately. The main features of a batch processing framework include the ability to handle data in parallel, optimize resource usage, and ensure data integrity throughout the process. Additionally, these frameworks often offer support for integration with various data sources and storage systems, allowing data engineers to build robust and efficient data pipelines. In the context of data processing technologies, batch processing is often combined with real-time processing, enabling users to leverage the best of both worlds, facilitating the creation of advanced and adaptive data analysis applications.
