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Foundry: a message-oriented, horizontally scalable ETL system for scientific data integration and enhancement
Ibrahim Burak Ozyurt1, Jeffrey S Grethe1
1Center for Research in Biological Systems, University of California, San Diego, La Jolla, CA, USA.
Database : the Journal of Biological Databases and Curation
|December 22, 2018
Summary
This study presents a scalable system and data transformation language to improve scientific data discovery and retrieval. It addresses the growing challenge of accessing and utilizing large biomedical datasets for research.
Area of Science:
- Biomedical Informatics
- Data Science
- Scientific Data Management
Background:
- Scientific research generates vast amounts of data, particularly in biomedicine.
- Finding relevant data for reanalysis and novel studies is a significant and growing challenge.
- Existing data aggregation and retrieval methods struggle with increasing data volumes and heterogeneity.
Purpose of the Study:
- To introduce a horizontally scalable distributed extract-transform-load (ETL) system for scientific data aggregation, transformation, and enhancement.
- To develop a data transformation language for biomedical curators to manage heterogeneous data sources.
- To facilitate scientific data discovery and retrieval in complex research environments.
Main Methods:
- Development of a horizontally scalable distributed ETL system.
- Introduction of a domain-specific data transformation language.
- Implementation and testing of the system across biomedical and earth science domains.
Main Results:
- The proposed ETL system effectively handles large-scale scientific data aggregation and transformation.
- The data transformation language enables efficient combination and manipulation of data from diverse sources.
- Demonstrated applicability in both biomedical and earth science contexts, improving data accessibility.
Conclusions:
- The developed system and language offer a robust solution for scientific data discovery and retrieval challenges.
- Enhanced data accessibility through aggregation and transformation can accelerate scientific advancement.
- The approach is adaptable to various scientific domains facing data management hurdles.
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