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I want to generate a map, using some spatial data provider like OSM. I am trying to put in some dynamic data-set on top of the base map. The data size is considerably large enough to be called as BigData. Its taking hours to insert data into PostGIS. NoSQL Databases are meant to be scalable for humongous data sets. So my query is can i replace my PostGIS database with NoSQL, considering the scale-out factor and the enormous growth of data.

If so which seems best MongoDB, Hbase or Neo4j.

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I'm a business analyst working in a software company that focuses on GIS based optimization platforms. As a matter of fact, we are working on this to improve our performance. I know that It is possible, I just don't know how you can achieve it. Please check following links, they include such useful information: mongodb.org/display/DOCS/Geospatial+Indexing paolocorti.net/2009/12/06/… gissolved.blogspot.com/2009/05/… for desktop you may have a look at geokoder.com/mongodb-plugin-for-quantum –  Anıl Çelik Jul 16 '12 at 12:53
Isn't NoSQL good when relationships are not important? Relationships are important in geocoding (not sure what you mean by georeferencing which usually refers to taking something like a photo or scan that lacks location data and giving it geospatial reference). Are you adding to your data a lot? Do you know that it will be faster to bulk load into a NoSQL system initially? –  Sean Jul 17 '12 at 0:41
I agree, but when factor of scaling out comes, NoSQL stands out. I mean not today but in the near future we may have enough unstructured data to be in maps. Google is using BigTable for their maps,foursquare is using MongoDB, Navteq uses hadoop. Some more here nathankerr.com/projects/parallel-gis-processing/… –  azzaxp Jul 17 '12 at 4:48

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