âDask has been a trailblazer in making distributed and out-of-memory computing in Python easy and accessible for everyone.â
Wes McKinney, Pandas
âAt Capital One, early implementations of Dask have reduced model training times by 91% within a few months of development effort.â
Ryan McEntee, Capital One
âMy climate science research has been made possible by Dask. Dask integrates seamlessly with Xarray, making it easy to run large-scale computations on multi-dimensional datasets. I can focus on my research instead of thinking about parallelism.â
Paige Martin, Pangeo
âDask shines when dealing with generic data structures which donât conform to table-like structures. PySpark has RDDs, but who wants to code in Python and debug verbose Java logs?â
Ajith Aravind, Simeio
âDask has transformed how the world interacts with weather, climate, and geospatial data by making it super easy to scale up data processing pipelines on HPC or cloud. Things that seemed impossible five years ago are now routine thanks to Dask.â
Ryan Abernathy, Earthmover
âWith Dask, I can easily adapt code that runs on a single machine and scale it across an entire cluster. Very few other tools let you get going that quicklyâacross any language.â
Jacqueline Nolis, Fanatics Inc.
âDask also makes it easy to deploy distributed work locally using multiple Python processes in a way that is nearly identical to how full production load is distributed.â
Hugues Demers, Grubhub
âTo further accelerate our usersâ ability to scale easily on the cloud, we expanded this by setting up pre-configured Horovod and Dask clusters.â
Meenakshi Sharma, Wayfair
âI used to work in Spark all the time. The sooner you move to Dask, the sooner youâll be grateful you did.â
John Renken, Rebuy
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