Data parallelism refers to a parallel computing technique where a large dataset is partitioned and processed in parallel across multiple computing resources within a directed acyclic graph (DAG) or pipeline. In data parallelism, each task in the DAG/pipeline operates on a different subset of the dataset in parallel, allowing for efficient processing of large amounts of data. The results of each task are then combined to produce the final output. Data parallelism is often used in machine learning and deep learning pipelines where large datasets need to be processed in parallel using multiple computing resources. By distributing the data across different nodes, data parallelism can help reduce the overall processing time and improve the performance of the pipeline.