Cloud Computing and Big Data
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1 Cloud Computing and Big Data Karl Benedict Earth Data Analysis Center, University Libraries, Department of Geography University of New Mexico EDAC
2 An Architecture Designed for Scalability Definitions of Cloud Computing and Big Data Development of an extensible Services Oriented Architecture for Research Data management, discovery & access Use of cloud-compatible software components and Launching into the Cloud
3 Cloud Computing? On-demand self-service Broad network Access Resource Pooling Rapid Elasticity Measured Service Mell P & Grance T (2011) The NIST Definition of Cloud Computing - Recommendations of the National Institute of Standards and Technology. (National Institute of Standards and Technology, Computer Science Division, Information Technology Laboratory, Gaithersburg, MD), p 7.
4 Cloud Computing? On-demand self-service Broad network Access Resource Pooling Rapid Elasticity Measured Service Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Mell P & Grance T (2011) The NIST Definition of Cloud Computing - Recommendations of the National Institute of Standards and Technology. (National Institute of Standards and Technology, Computer Science Division, Information Technology Laboratory, Gaithersburg, MD), p 7.
5 Cloud Computing? On-demand self-service Broad network Access Resource Pooling Rapid Elasticity Measured Service Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Mell P & Grance T (2011) The NIST Definition of Cloud Computing - Recommendations of the National Institute of Standards and Technology. (National Institute of Standards and Technology, Computer Science Division, Information Technology Laboratory, Gaithersburg, MD), p 7.
6 Cloud Computing? On-demand self-service Private Cloud Broad network Access Community Cloud Resource Pooling Public Cloud Rapid Elasticity Hybrid Cloud Measured Service Software as a Service (SaaS) Platform as a Service (PaaS) Infrastructure as a Service (IaaS) Mell P & Grance T (2011) The NIST Definition of Cloud Computing - Recommendations of the National Institute of Standards and Technology. (National Institute of Standards and Technology, Computer Science Division, Information Technology Laboratory, Gaithersburg, MD), p 7.
7 Cloud Computing? Current Characteristics and Capabilities On-demand self-service Broad network Access Resource Pooling Software as a Service (SaaS) Private Cloud Future Characteristics and Capabilities Rapid Elasticity Measured Service Platform as a Service (PaaS) Hybrid Cloud
8 Big Data Many Problems and Solutions* NASA - Goddard Space Flight Center NOAA - National Climate Data Center
9 Big Data Sample Solutions Horizontal vs. vertical scaling Unstructured and semi-structured data models Parallelism Linked analytics Machine learning
10 Big Data An Aligned Definition An easily scalable system of unstructured data with accompanying tools that can efficiently pull structured datasets. * Definition provided by John Denver as a comment on the FCW Blog Post of 4/15/2013 Entitled Sketching the big picture on big data
11 EDAC s Data Challenge A Snapshot
12 EDAC s Data Challenge A Snapshot NM%EPSCoR% 281,315%datasets% 9,054%files% 169,981%vectors% 102,280%rasters% RGIS% 120,031%datasets% 12,171%files% 11,808%vectors% 96,052%rasters% 10.5TB%on%disk% 140GB%MongoDB%(22GB%Postgres)% 114%million%documents% 1.115%billion%data%points% 1.5%million%discrete,%downloadable%data%objects%
13 EDAC s Data Challenge A Snapshot Metadata Vectors Rasters Files Services FGDC SHP GeoTIFF ZIP WMS FGDC-RSE KML IMG HTML WFS ISO / GML SID PDF WCS NM%EPSCoR% 281,315%datasets% 9,054%files% 169,981%vectors% 102,280%rasters% RGIS% 120,031%datasets% 12,171%files% 11,808%vectors% 96,052%rasters% ISO GeoJSON ECW DOC/DOCX ISO JSON DEM GZ 10.5TB%on%disk% 140GB%MongoDB%(22GB%Postgres)% 114%million%documents% 1.115%billion%data%points% 1.5%million%discrete,%downloadable%data%objects% CSV ASCII XLS/XLSX XLS PPT/PPTX
14 Client.Applica4ons. EDAC s Data Challenge Our Solution Geoportal.Server. OAIIPMH/OGCICSW/z39.50/HTTP. HTTP/HTTPS. Data.Management. Services. REST.Services.(loadIbalanced.applica4on.cluster). Search. Download.Data. Admin. Metadata. Stream.Data. MongoDB. Sharded.replica. set. DataONE. API. OGC.Services. WMS. WFS. PostgreSQL/ PostGIS. (loadibalanced.pgi Pool). CUAHSI. API. WAF. WCS. Geoportal.Server. File.System. PostgreSQL/PostGIS.. Dataset.metadata. Spa4al.data. MongoDB.. Vector.a;ribute.data. File.system. Rasters. Compressed.datasets. Dataset.cache.
15 Client.Applica4ons. Cloud Computing & Big Data? (Now) Geoportal.Server. OAIIPMH/OGCICSW/z39.50/HTTP. HTTP/HTTPS. Data.Management. Services. REST.Services.(loadIbalanced.applica4on.cluster). Search. Download.Data. Admin. Metadata. Stream.Data. MongoDB. Sharded.replica. set. DataONE. API. OGC.Services. WMS. WFS. PostgreSQL/ PostGIS. (loadibalanced.pgi Pool). CUAHSI. API. WAF. WCS. Geoportal.Server. File.System. PostgreSQL/PostGIS.. Dataset.metadata. Spa4al.data. MongoDB.. Vector.a;ribute.data. File.system. Rasters. Compressed.datasets. Dataset.cache.
16 Client.Applica4ons. Cloud Computing & Big Data? (Now) Geoportal.Server. OAIIPMH/OGCICSW/z39.50/HTTP. HTTP/HTTPS. Data.Management. Services. REST.Services.(loadIbalanced.applica4on.cluster). Search. Download.Data. Admin. Metadata. Stream.Data. MongoDB. Sharded.replica. set. DataONE. API. OGC.Services. WMS. WFS. PostgreSQL/ PostGIS. (loadibalanced.pgi Pool). CUAHSI. API. WAF. WCS. Geoportal.Server. File.System. PostgreSQL/PostGIS.. Dataset.metadata. Spa4al.data. MongoDB.. Vector.a;ribute.data. File.system. Rasters. Compressed.datasets. Dataset.cache.
17 Client.Applica4ons. Cloud Computing & Big Data? (Future) Geoportal.Server. OAIIPMH/OGCICSW/z39.50/HTTP. HTTP/HTTPS. Data.Management. Services. REST.Services.(loadIbalanced.applica4on.cluster). Search. Download.Data. Admin. Metadata. Stream.Data. MongoDB. Sharded.replica. set. DataONE. API. OGC.Services. WMS. WFS. PostgreSQL/ PostGIS. (loadibalanced.pgi Pool). CUAHSI. API. WAF. WCS. Geoportal.Server. File.System. PostgreSQL/PostGIS.. Dataset.metadata. Spa4al.data. MongoDB.. Vector.a;ribute.data. File.system. Rasters. Compressed.datasets. Dataset.cache.
18 In Conclusion Questions?
19 Acknowledgements Support New Mexico Leglature - NM RGIS Program National Science Foundation EPSCoR Program - Awards , (in collaboration with ID and NV EPSCoR programs) NASA - ACCESS Award NNX12AF52A (in collaboration with UTEP, KU & PNNL) Lead Developer: Soren Scott Contributions by: Renzo Sanchez-Silva, William Hudspeth
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