Big Data – Tap into Cloud Infrastructure with FME

Editor's Notes

• #10: Video plays here - what is big dataFuzzy term sort of like “cloud”. What does big data look like?As a catch-all term, “big data” can be pretty nebulous, in the same way that the term “cloud” covers diverse technologies. Input data to big data systems could be chatter from social networks, web server logs, traffic flow sensors, satellite imagery, broadcast audio streams, banking transactions, MP3s of rock music, the content of web pages, scans of government documents, GPS trails, telemetry from automobiles, financial market data, the list goes on. Are these all really the same thing? To clarify matters, the three Vs of volume, velocity and variety are commonly used to characterize different aspects of big data. They’re a helpful lens through which to view and understand the nature of the data and the software platforms available to exploit them. Most probably you will contend with each of the Vs to one degree or another.
• #12: Big data holds all of it
• #14: - on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data
• #15: Loading DataConversion: big data not spatial friendly (CAD, GIS)Expensive to upload / downloadGeoreferencing and spatial indexingmost big data repositories have limited geospatialBig Data AnalysisQuerying and Exporting DataTricky to find and access stored dataNeed to generate appropriate keys on load
• #16: Loading DataConversion: big data not spatial friendly (CAD, GIS)Expensive to upload / downloadGeoreferencing and spatial indexingmost big data repositories have limited geospatialBig Data AnalysisQuerying and Exporting DataTricky to find and access stored dataNeed to generate appropriate keys on load
• #17: Loading DataConversion: big data not spatial friendly (CAD, GIS)Expensive to upload / downloadGeoreferencing and spatial indexingmost big data repositories have limited geospatialBig Data AnalysisQuerying and Exporting DataTricky to find and access stored dataNeed to generate appropriate keys on load
• #18: Big data repository – scale as big as you wantNoSQL database – optimized for XML / GMLPowerful search and analysis (BI, semantic queries)Stores location, not just geohashXML based data model – rapid XML exportStore any documents: GML, XML (metadata)Deploy on Hadoop HDFS
• #19: * As applicable (e.g. cant convert raster to gml!)FME2014’s new schema based GML writer which allows FME to convert almost any CAD / GIS or even BIM data to GML or CityGML. This makes FME a very powerful loader tool for MarkLogicFME - A Natural Fit to support MarkLogic:Converts almost any spatial data to GMLWrite almost any XML with XMLTemplaterLoading XML into MarkLogic is a simple HTTP PUT operation easily done with HTTPUploaderQuery, process and reconvert XML results
• #21: Converting features to GML/XML usually involves a GeometryExtractor transformer or some combination of CoordinateExtractor and XMLTemplaterKey fields can be captured from the source data or use UUIDGenerator to generate unique IDs for URIs etc.Build insert message with XMLTemplaterExecute REST PUT call with HTTPUploader
• #22: Converting features to GML/XML usually involves a GeometryExtractor transformer or some combination of CoordinateExtractor and XMLTemplaterKey fields can be captured from the source data or use UUIDGenerator to generate unique IDs for URIs etc.Build insert message with XMLTemplaterExecute REST PUT call with HTTPUploader<?xml version="1.0" encoding="UTF-8"?><xml><docID>{fme:get-attribute("_uuid")}</docID><docAuthor>{fme:get-attribute("user")}</docAuthor><modType>{fme:get-attribute("updateType")}</modType><UpdateDate>{fme:get-attribute("_timestamp")}</UpdateDate><filePath>{fme:get-attribute("filePath")}</filePath><comment>{fme:get-attribute("comment")}</comment><doc_xml>{fme:get-xml-attribute("_file_contents")}</doc_xml></xml>
• #23: As simple as 1,2,3,4!
• #24: - on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data
• #25: * need bubble here for XML/WFS – maybe a circle with something like this in it:<gml:featureMember> <gn:NamedPlacegml:id=“abc.123"> <gn:geometry> <gml:Pointgml:id=“p.abc.123" srsName="EPSG:4258"><gml:pos>15.2 36.7</gml:pos> </gml:Point> </gn:geometry>…
• #27: This workspace can support the retrieval of any type of XML/GML regardless of schema. The same query workspace can be used to retrieve AIXM, INSPIRE or any other type of XML/GML.StringConcatenator composes search GET request based on input parametersHTTPFetcher sends search GET request to MarkLogicXMLFlattener flattens the response so result.uri can be exposedSecond StringConcatenatorcomposes document GET request based on matching URISecond HTTPFetcher sends document retrieval GET request to MarkLogicXMLFragmenter pulls out the doc_xml from the MarkLogic responseXML writer outputs the XML as a file or streams it to the FMEServer client once workspace is publishedSearch GET request to find URI based on query:http://localhost:8003/v1/keyvalue?element=comment&value=AIXM.ChicagoDocument Retrieval GET request based on URI:http://localhost:8003/v1/documents?uri=/docs/myXML_653c46c3-fdfb-4837-ae1c-49735dd29356.xml
• #28: For this demo the previous workspace was published to FME Server to make a feature service hosted by FMEServer on top of MarkLogic. The example here supports a simple REST based XML data stream.We could easily use this approach to build a FMEServer hosted WFS on top of MarkLogic.
• #29: This demo shows Inspector reading AIXM5 GML directly from the GET query: http://UHURA/fmedatastreaming/Demos/QueryMarkLogicDB.fmw?Element=airportCode&Value=CYVRThe query goes to FMEServer’s data streaming serviceFMEServer uses the URL parameters to run the published QueryMarkLogicDB.fmw workspace.QueryMarkLogicDB.fmw uses the values of Element and Value to build a search request and send that to MarkLogicQueryMarkLogicDB.fmw uses the URI from MarkLogic’s search result to compose and submit a document request to MarkLogicQueryMarkLogicDB.fmw extracts the feature XML from the MarkLogic’s document response and streams it back to the FMEServer client
• #30: This just shows how FME can read XML from MarkLogic and use the GeometryReplacer to covert it to virtually any format FME supports
• #31: Shows how FME can be used to integrateMarkLogic and ArcGIS Server.These are the steps to move data from MarkLogic to Arc Server Feature Service
• #32: Shows how FME can be used to integrateMarkLogic and ArcGIS Server.These are the steps to move data from Arc Server Feature Service to MarkLogic. Note this workflow could be event driven, real time or as a scheduled update.
• #33: Workspace showing data flow from ArcServer toMarkLogic. REST call to feature service retrieves the feature of interest.JSON is extracted and GeometryReplacer generates an FME geometry from it.GeometryExtractor renders the FME geometry as GMLGML is added to an XML update message and posted to MarkLogic
• #34: Demo #2 Limitless Spatial Indexed Database:Geohash spatial indexStore Vector DataStore Raster DataStore Lidar DataStore geotagged images by locationStore and associate any document with a location
• #41: - on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data