Matthew Rocklin
where else to find them
what they talk about
all 8 in the filters
where they keep going back
- PyData Global2 editions2022–2023
- PyData NYC2 editions2022–2023
all 8 in the filters
on a stage with
- Aron Ahmadiaonce
- Patrick Hoefleronce
who talks about the same things
- Jim Crist5 talks
- Hendrik Makait4 talks
- Patrick Hoefler11 talks
- Richard Pelgrim2 talks
- Julia Signell4 talks
- Martin Durant8 talks
| title | conference | date |
|---|---|---|
| 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… | ||
| Spark, Dask, DuckDB, and Polars: Benchmarks | PyData NYC 2023 | 2023-11-01 |
Benchmarks run Spark, Dask, DuckDB and Polars on scales from 10 GiB to 10 TiB using TPC-H on a local M1 MacBook Pro and AWS instances, with orchestration via… | ||
| Deploying Dask | PyData Global 2022 | 2022-12-01 |
Requesting 20 machines with two cores each on AWS provisions T3 mediums in 20–30 seconds, pip/conda packages and local Git code get installed, and the Optuna +… | ||
| Deploying Dask | PyData NYC 2022 | 2022-11-09 |
Deploying Dask is messy but solvable: an Optuna + XGBoost demo requires a MySQL replacement, spins up 20 machines on AWS, and surfaces a profile where 97%… | ||
| Dask at Global Scale with | PyData Eindhoven 2020 | 2020-10-07 |
Claim: Coiled offers managed Dask so users get a real cluster from two commands — pip install coiled and a couple Python calls — with software environments… | ||
| Deploying Python at Scale with Dask | PyCon US 2020 | 2020-04-15 |
Dask claims to make NumPy/Pandas/Scikit-Learn scale to 100+TB datasets and runs on Kubernetes, Yarn and cloud VMs; deployments actually use containers, TLS… | ||
| Democratizing Distributed Computing with Dask and JupyterHub | PyCon 2018 | 2018-05-11 |
A cloud deployment combines JupyterHub, JupyterLab, XArray and Dask on Kubernetes to let scientists run real analyses: a 70 GB sea‑level array is split across… | ||
| Dask: Next Steps in Parallel Python | PyCon.DE 2017 | 2017-10-25 |
Dask demonstrates running familiar pandas code on a cluster by breaking a 20 gigabytes on disk dataset into ~300 blocks, executing on a 64 cores cluster with a… | ||
| Dask for ad hoc distributed computing | PyData DC 2016 | 2016-10-07 |
Describes a pure-Python dynamic task scheduler that executes arbitrary task graphs, coordinates workers and a central scheduler, and measures runtimes and byte… | ||
| Using Dask for Parallel Computing in Python | PyData DC 2016 | 2016-10-07 |
Dask provides parallel arrays, bags, and dataframes that build task graphs from many small NumPy or pandas pieces, run on threads, processes, or a distributed… | ||
| Parallel Python Analyzing Large Data Sets | PyData DC 2016 | 2016-10-07 |
Shows concrete patterns and results: using concurrent.futures process/thread pools to turn a JSON-to-HDF5 conversion from slow sequential loops into a parallel… | ||
| keynote | PyData Berlin 2015 | 2015-05-29 |
Claim: a usable out-of-core multicore numpy clone exists that turns array expressions into task graphs and runs them on one machine. It uses blocked algorithms… | ||
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