s3-parquet-sampling

Sampling large Parquet files on S3 reduces the dataset to a manageable size for interactive exploration. This approach uses Dask for scalable processing and caches the reduced data locally as Parquet to avoid repeated downloads.

Loading full large Parquet datasets from S3 consumes significant memory because the entire dataset is transferred and processed at once. Applying sampling and local caching mitigates this by storing only a reduced subset locally for analysis.

Yes, you can use Dask and Datashader to visualize big data stored in S3 Parquet files. The workflow samples the large dataset first and caches it locally, enabling hvPlot and Datashader to render scalable visualizations quickly.

The best way to avoid repeated downloads of S3 Parquet data is local Parquet caching. By caching the sampled dataset locally after the initial retrieval, subsequent data exploration and visualization workflows run much faster.

Yes, sampling S3 Parquet data works for data-heavy hvPlot workflows. By reducing the dataset size and caching it locally, hvPlot can handle the visualization requirements efficiently without hitting memory limits during interactive data exploration.