Cloud computing offers unparalleled flexibility, a constantly increasing set of “Infrastructure as a Service’’ capabilities, resource elasticity and security isolation. One of the most significant barriers in astronomy to wholesale adoption of cloud infrastructures is the cost for hot storage of large datasets - particularly for Rubin, a Big Data project sized at 0.5 Exabytes (500 Petabytes) over the duration of its ten-year mission. We are planning to reconcile this with a “hybrid” model where user-facing services are deployed on Google Cloud with the majority of data holdings residing in our on-premises Data Facility at SLAC. We discuss the opportunities, status, risks, and technical challenges of this approach.
The Rubin Observatory’s Data Butler is designed to allow data file location and file formats to be abstracted away from the people writing the science pipeline algorithms. The Butler works in conjunction with the workflow graph builder to allow pipelines to be constructed from the algorithmic tasks. These pipelines can be executed at scale using object stores and multi-node clusters, or on a laptop using a local file system. The Butler and pipeline system are now in daily use during Rubin construction and early operations.
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