  We present the Rubin Observatory system for data storage/retrieval and
pipelined code execution. The layer for data storage and retrieval is named the
Butler. It consists of a relational database, known as the registry, to keep
track of metadata and relations, and a system to manage where the data is
located, named the datastore. Together these systems create an abstraction
layer that science algorithms can be written against. This abstraction layer
manages the complexities of the large data volumes expected and allows
algorithms to be written independently, yet be tied together automatically into
a coherent processing pipeline. This system consists of tools which execute
these pipelines by transforming them into execution graphs which contain
concrete data stored in the Butler. The pipeline infrastructure is designed to
be scalable in nature, allowing execution on environments ranging from a laptop
all the way up to multi-facility data centers. This presentation will focus on
the data management aspects as well as an overview on the creation of pipelines
and the corresponding execution graphs.
