Patrick Hoefler
where else to find them
what they talk about
all 6 in the filters
where they keep going back
- PyCon US2 editions2024–2025
all 5 in the filters
on a stage with
- Matthew Rocklinonce
who talks about the same things
- Matthew Rocklin13 talks
- Han Wang13 talks
- Matt Harrison16 talks
- Kevin Kho7 talks
- Uwe L. Korn9 talks
- Martin Durant8 talks
| title | conference | date |
|---|---|---|
| Processing Large Geospatial Datasets with Dask & Xarray | PyCon US 2025 | 2025-05-16 |
Demonstrates using Xarray and Dask Array to process ERA5 and other public datasets at hundreds of terabytes scale with concrete numbers like a 250 TB example… | ||
| Building Large Scale ETL Pipelines with Dask | PyData Paris 2024 | 2024-09-25 |
New Dask DataFrame implementation claims concrete wins: switching to pyarrow-backed strings cuts string-heavy memory use by over 50% and speeds some string ops… | ||
| Dask DataFrame 2.0: Comparison to Spark, DuckDB and Polars | PyData London 2024 | 2024-06-14 |
Reimplemented Dask DataFrame claims roughly 20x speedups on representative TPCH queries by combining Arrow strings, a new shuffle algorithm and a query… | ||
| Pandas + Dask DataFrame 2.0 - Comparison to Spark, DuckDB and Polars | PyCon US 2024 | 2024-05-15 |
Dask integrates pandas 2.0, Arrow strings and a rewritten P2P shuffle plus a lightweight logical query planning layer. Benchmarks use TPC-H at 100 GB / 1 TB /… | ||
| Arrow revolution in pandas and Dask | PyData Global 2023 | 2023-12-06 |
Claims are concrete: Arrow-backed strings cut memory (example: three string columns drop ~220 GB to ~100 GB), copy and right turns defensive copies into a fast… | ||
| The Arrow revolution in pandas and Dask | PyData NYC 2023 | 2023-11-01 |
Pandas and Dask adopt Apache Arrow so string columns become contiguous and memory efficient and parquet reads can be zero‑copy leading to 50–60% memory… | ||
| Dask tutorial | PyData NYC 2023 | 2023-11-01 |
Shows Dask (local and distributed) used to parallelize Python: turning slow for‑loops into futures‑based parallel tasks, scaling pandas workflows to a 100 GB… | ||
| nothing matches | ||