See how to easily migrate to the cloud with FME, and take advantage of the corresponding benefits including unlimited resources, scalability, and zero hardware to maintain. You'll see how you can use new FME 2014 support to move data to Big Data handling tools and services such as Amazon RDS, Amazon S3, Amazon DynamoDB, Amazon RedShift, and Google BigQuery. Plus, learn about the benefits of being close to the data, and how FME Server and FME Cloud can help power the flow of data, whether it's hosted, on-site, or somewhere in between.
2. Meet the presenters.
Don Murray
President and Co-Founder
@DonAtSafe
Dean Hintz
Senior Product Specialist
@DeanAtSafe
3. Ask us. And join the discussion.
Please submit using the
GoToWebinar panel.
We will follow-up with
unanswered questions.
4. Agenda.
What is Big Data
Big Data Challenges
FME and Big Data
FME Demos:
Loading and Extracting from
MarkLogic
Spatial Indexing and Loading to
DynamoDB
8. New to FME?
Get your bearings from our Getting
Started Page:
www.safe.com/fme/getting-started
Learn from our crew in one of the
weekly FME Overview webinars:
safe.com/WeeklyIntro
10. Big Data and Cloud
Big Data needs big resources
Big datastores
Big processing power
Big bandwidth
Cloud technology gives you this for fraction of
traditional cost!
11. Big Data and FME
Big Data is a new data
“classification” for FME.
Big Data is no different than
other data to FME
FME Cloud is a natural fit for
data in the Cloud
FME makes it easy to leverage the power of Big Data.
12. Big Data and FME Support
Amazon S3
Limitless internet based
storage
Amazon RDS
See blog article on Amazon RDS (PostGIS)
Amazon DynamoDB
NoSQL limitless database service
Amazon RedShift
Petabyte scale database warehouse service.
Google BigQuery
Superfast append only tables
MarkLogic
Large XML based database
16. Why Demo FME with
MarkLogic and DynamoDB?
Different from other
databases supported by
FME.
17. What is ?
NoSQL database – XML optimized
Powerful search and analysis
Native Spatial Support
XML based data model (GML, XML, etc.)
Deploy on Hadoop HDFS
18. FME and MarkLogic – A Natural Fit
Convert data to XML/GML*
Easily Load XML into MarkLogic with FME
Process and convert XML results
FME 2014: New schema based GML Writer
19. Demo #1a Loading MarkLogic
Convert GIS / CAD
data to GML (XML)
Compose REST request
to PUT to MarkLogic
database
20. 1. Convert GIS / CAD data into Valid GML
2.Generate Key Fields
3. Build insert message
4. Execute PUT REST call
MarkLogic accepts any valid XML – just PUT it!
Loading GIS to MarkLogic
23. Demo #1b Exporting from MarkLogic
GET Query to find
URI’s for features
of interest
GET Query using URI’s to
get feature XML/GML,
then
Conversion to format of
choice (CAD, GIS …)
/WFS
24. Exporting XML from MarkLogic
1. Query database via GET request
2. Parse search result and compose GET feature request
3. Extract attributes and geometry from result
4. Validate and Write XML Result
25. Exporting XML from MarkLogic
Search GET request:
http://localhost:8003/v1/keyvalue?element=comment&value=AIXM.Chicago
Retrieval GET request:
http://localhost:8003/v1/documents?uri=/docs/myXML_653c46c3-fdfb-4837-ae1c-
49735dd29356.xml
26. AIXM from MarkLogic via FMEServer
http://UHURA/fmedatastreaming/Demos/QueryMarkLogicDB.fmw
?Element=airportCode&Value=CYVR
/AIXM
29. MarkLogic to ArcGIS via FME Server:
1. Submit search to MarkLogic as described earlier
2. Extract attributes and geometry from result
3. Generate update ESRIJSON message from feature
4. Post update ESRIJSON to ArcGIS Server
MarkLogic / ArcGIS Integration
30. ArcGIS Server to MarkLogic
via FME Server
1. Retrive JSON data from ArcGIS Server
2. Generate output GML
3. Write data to MarkLogic via PUT REST call
40. Save the date.
Webinar: How to Automate Practically
Anything with FME Server (March 25th)
Webinar: How to Load Data into Google
Maps Engine (April 16th)
FME World Tour 2014 (April – June 2014)
FME International User Conference 2014
(20th Anniversary Celebration)
• June 10 – 13, 2014 in Vancouver, Canada
41. Free and fun to learn.
Online Courses - Live & Hands-On
Feb 18-19: FME Desktop
Tutorials & Recorded Courses
43. Summary
Big Data = big new opportunities
FME great for working with Big Data
Cloud model is a natural fit for Big Data
This is just the beginning - more to come!
44. Hand raising has now
been enabled.
If you’d like to ask a
question over the
air, please click the
hand icon and
ensure your audio
input is set up.
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.
Big data holds all of it
- on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data
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
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
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
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
* 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
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
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>
As simple as 1,2,3,4!
- on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data
* 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>…
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
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.
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
This just shows how FME can read XML from MarkLogic and use the GeometryReplacer to covert it to virtually any format FME supports
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
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.
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
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
- on premise - cloud (amazon web services) - cloud (google) - cloud (other) - not currently using Big Data