dask

To process pandas DataFrames larger than available RAM, use out-of-core execution to chunk data across partitions. This enables parallel file processing and scalable ETL aggregations without loading the entire dataset into memory at once.

The best way to parallelize NumPy array computations is using chunked arrays that execute operations in parallel across multiple cores. This approach handles large numerical workloads by dividing arrays into manageable blocks for scientific and image processing.

Yes, you can run distributed ETL pipelines on multiple Parquet files by reading them as a single partitioned DataFrame. Apply map_partitions to clean and aggregate data, persist intermediates, and write results back to Parquet without exceeding memory limits.

Out-of-core dataframe processing supports threading, multiprocessing, and distributed schedulers. You can select the appropriate scheduler for dynamic task-based workflows and monitor execution progress using a dedicated diagnostic dashboard.

To process unstructured JSON and log files memory-efficiently, use a streaming bag abstraction that handles text data in chunks. This allows you to filter and transform unstructured records before converting them into structured DataFrames.

You should avoid parallel processing for pandas workflows when datasets fit comfortably in RAM, as the overhead of task graph construction and chunked data management outweighs the benefits of distributed execution on small in-memory computations.