ray-data

You can scale ML data preprocessing across distributed clusters using Ray Data. It applies batch transforms and streaming ETL to large datasets, scaling seamlessly from a single machine to multi-node environments.

Loading Parquet, CSV, and JSON files for distributed ML pipelines is handled natively by Ray Data. It processes these formats in streaming workflows to prevent out-of-memory errors on large datasets.

Yes, Ray Data integrates directly with PyTorch and TensorFlow. It works alongside Ray Train to provide end-to-end ML pipelines, handling scalable data loading and preprocessing before model training.

Yes, you can use Ray Data for batch feature engineering on multi-modal datasets. It supports scalable preprocessing across images, text, and tabular data within distributed ML workflows.

You need pandas and pyarrow installed alongside the ray[data] dependency to run Ray Data pipelines. These libraries handle underlying data structures and format parsing for your ML preprocessing tasks.

Use distributed data processing when local pandas workflows hit memory limits with large datasets. Ray Data streams batch transforms across clusters, bypassing single-machine bottlenecks for scalable ML pipelines.