SDAP is a technology software solution currently geared to better enable scientists involved in advancing the study of the Earth's physical oceanography. With increasing global temperature, warming of the ocean, and melting ice sheets and glaciers, the impacts can be observed from changes in anomalous ocean temperature and circulation patterns, to increasing extreme weather events and stronger/more frequent hurricanes, sea level rise and storm surges affecting coastlines, and may involve drastic changes and shifts in marine ecosystems. Ocean science communities are relying on data distributed through data centers such as the JPL's Physical Oceanographic Data Active Archive Center (PO.DAAC) to conduct their research. In typical investigations, oceanographers follow a traditional workflow for using datasets: search, evaluate, download, and apply tools and algorithms to look for trends. While this workflow has been working very well historically for the oceanographic community, it cannot scale if the research involves massive amount of data. NASA's Surface Water and Ocean Topography (SWOT) mission, scheduled to launch in April of 2021, is expected to generate over 20PB data for a nominal 3-year mission. This will challenge all existing NASA Earth Science data archival/distribution paradigms. It will no longer be feasible for Earth scientists to download and analyze such volumes of data. SDAP was therefore developed primarily as a Web-service platform for big ocean data science at the PO.DAAC with open source solutions used to enable fast analysis of oceanographic data. SDAP has been developed collaboratively between JPL, FSU, NCAR, and GMU and is rapidly maturing to become the generic platform for the next generation of big science data solutions. The platform is an orchestration of several previously funded NASA big ocean data solutions using cloud technology, which include data analysis (NEXUS), anomaly detection (OceanXtremes), matchup (DOMS), subsetting, discovery (MUDROD), and visualization (VQSS). SDAP will enable web-accessible, fast data analysis directly on huge scientific data archives to minimize data movement and provide access, including subset, only to the relevant data. In essence, the above information workflow can be visualized by the image below where a transformation of data to knowledge occurs as one moves from left to right.