Bruno Grande
Bruno Grande

sagetasks: a Python package for data and workflow orchestration in the cloud

Ecosystem
Bruno M. Grande, Tess M. Thyer, James A. Eddy, Thomas V. Yu, Brian D. O’Connor

Workflow execution engines such as Nextflow provide several benefits for processing scientific data, including scalability, portability, and caching. However, these workflows can often be part of larger extract-transform-load (ETL) pipelines because (1) input and output data are stored in different locations (common in the cloud context) and (2) multiple community-curated (e.g., nf-core) workflows need to be chained together, among other reasons. These use cases motivated us to develop the sagetasks Python package. This tool is a growing collection of reusable functions for orchestrating data and workflows on various platforms. We aim to be platform-agnostic and leverage standard APIs where possible, such as the GA4GH Workflow Execution Service. We hope this effort will facilitate a variety of workflow needs, including data coordination projects and model-to-data challenges. We therefore believe that sagetasks can be a valuable addition to the Nextflow Tower ecosystem.

sagetasks: a Python package for data and workflow orchestration in the cloud

sagetasks: a Python package for data and workflow orchestration in the cloud

Ecosystem
Bruno Grande
Bruno Grande
Bruno Grande

Bruno Grande

Senior Research Software Engineer at Sage

Ecosystem
Poster presenter