Horizon: The Portable, Scalable, and Reusable Framework for Developing Automated Data Management and Product Generation Systems

Tuesday, 16 December 2014: 5:18 PM
Thomas Huang, Christian Alarcon and Nga T Quach, NASA Jet Propulsion Laboratory, Pasadena, CA, United States
Capture, curate, and analysis are the typical activities performed at any given Earth Science data center. Modern data management systems must be adaptable to heterogeneous science data formats, scalable to meet the mission’s quality of service requirements, and able to manage the life-cycle of any given science data product. Designing a scalable data management doesn’t happen overnight. It takes countless hours of refining, refactoring, retesting, and re-architecting. The Horizon data management and workflow framework, developed at the Jet Propulsion Laboratory, is a portable, scalable, and reusable framework for developing high-performance data management and product generation workflow systems to automate data capturing, data curation, and data analysis activities. The NASA’s Physical Oceanography Distributed Active Archive Center (PO.DAAC)’s Data Management and Archive System (DMAS) is its core data infrastructure that handles capturing and distribution of hundreds of thousands of satellite observations each day around the clock. DMAS is an application of the Horizon framework. The NASA Global Imagery Browse Services (GIBS) is NASA’s Earth Observing System Data and Information System (EOSDIS)’s solution for making high-resolution global imageries available to the science communities. The Imagery Exchange (TIE), an application of the Horizon framework, is a core subsystem for GIBS responsible for data capturing and imagery generation automation to support the EOSDIS’ 12 distributed active archive centers and 17 Science Investigator-led Processing Systems (SIPS). This presentation discusses our ongoing effort in refining, refactoring, retesting, and re-architecting the Horizon framework to enable data-intensive science and its applications.