Difference between revisions of "Earth Science Data Analytics/Use Case Collection"
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'''Use Case Name''': MERRA Analytics Services | '''Use Case Name''': MERRA Analytics Services | ||
− | Provided By: John Schnase (entered by Steve K) | + | '''Provided By''': John Schnase (entered by Steve K) |
− | Brief Description: Combines iRODS-based Climate Data Server (CDS) capabilities with Cloudera MapReduce to serve MERRA analytic products | + | '''Brief Description''': Combines iRODS-based Climate Data Server (CDS) capabilities with Cloudera MapReduce to serve MERRA analytic products |
− | Key Analytics Needs: Store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions; Manage storage-side <driver, mapper, reducer> code sets and realized objects for users; and Provide a library of commonly used spatiotemporal operations (canonical ops) that can be composed to enable higher-order analyses. | + | '''Key Analytics Needs''': Store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions; Manage storage-side <driver, mapper, reducer> code sets and realized objects for users; and Provide a library of commonly used spatiotemporal operations (canonical ops) that can be composed to enable higher-order analyses. |
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Revision as of 18:51, April 6, 2014
Use Case Collecting...
Please insert the pertinent information regarding your use case below. We can beef up the details later. Alternatively, e-map information to Steve Kempler (Steven.J.Kempler@nasa.gov)
Use Case Name: MERRA Analytics Services
Provided By: John Schnase (entered by Steve K)
Brief Description: Combines iRODS-based Climate Data Server (CDS) capabilities with Cloudera MapReduce to serve MERRA analytic products
Key Analytics Needs: Store the MERRA reanalysis data collection in an HDFS to enable parallel, high-performance, storage-side data reductions; Manage storage-side <driver, mapper, reducer> code sets and realized objects for users; and Provide a library of commonly used spatiotemporal operations (canonical ops) that can be composed to enable higher-order analyses.
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