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\begin{document}

\title{Multi-Cloud workflows with Pangeo and Dask Gateway}



\author[1]{Tom Augspurger}%
\author[2]{Martin Durant}%
\author[3]{Ryan Abernathey}%
\author[4]{Joe Hamman}%
\affil[1]{taugspurger@continuum.io}%
\affil[2]{Anaconda Inc.}%
\affil[3]{Columbia University}%
\affil[4]{CarbonPlan}%


\vspace{-1em}



  
  \date{May 24, 2024}


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\begin{abstract}
As more analysis-ready datasets are provided on the cloud, we need to
consider how researchers access data. To maximize performance and
minimize costs, we move the analysis to the data. This notebook
demonstrates a Pangeo deployment connected to multiple Dask Gateways to
enable analysis, regardless of where the data is stored. Public clouds
are partitioned into regions, a geographic location with a cluster of
data centers. A dataset like the National Water Model Short-Range
Forecast is provided in a single region of some cloud provider (e.g.
AWS's us-east-1). To analyze that dataset efficiently, we do the
analysis in the same region as the dataset. That's especially true for
very large datasets. Making local ``dark replicas'' of the datasets is
slow and expensive. In this notebook we demonstrate a few open source
tools to compute ``close'' to cloud data. We use Intake as a data
catalog, to discover the datasets we have available and load them as an
xarray Dataset. With xarray, we're able to write the necessary
transformations, filtering, and reductions that compose our analysis. To
process the large amounts of data in parallel, we use Dask. Behind the
scenes, we've configured this Pangeo deployment with multiple Dask
Gateways, which provide a secure, multi-tenant server for managing Dask
clusters. Each Gateway is provisioned with the necessary permissions to
access the data. By placing compute (the Dask workers) in the same
region as the dataset, we achieve the highest performance: these worker
machines are physically close to the machines storing the data and have
the highest bandwidth. We minimize cost by avoiding egress costs: fees
charged to the data provider when data leaves a cloud region.%
\end{abstract}%



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