ray-data

Streaming ETL processes large ML datasets by applying distributed data transformations across clusters. It enables continuous batch preprocessing and loading, supporting formats like Parquet, CSV, and JSON for multi-modal data workloads.

Ray Data integrates seamlessly with PyTorch, TensorFlow, and Ray Train to support end-to-end ML pipelines. This compatibility allows you to load and transform distributed datasets directly into your training workflows without friction.

Batch preprocessing for large ML datasets is best handled by applying map_batches transformations across a distributed cluster. This method efficiently processes large volumes of data and writes the transformed results directly to Parquet files.

Distributed data processing supports reading and writing Parquet files natively. It also handles CSV and JSON formats, allowing you to easily ingest raw data, apply transformations, and export the results back to your preferred format.

Batch inference on multi-modal data is supported across multi-node clusters by distributing the workload. This setup scales your data pipelines efficiently, utilizing both CPUs and GPUs to process large inference tasks in parallel.