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GIS: All content tagged as GIS in NoSQL databases and polyglot persistence

GIS Tools for Hadoop by Esri

Interesting project, GIS Tools for Hadoop:

GIS Tools for Hadoop is an open source toolkit intended for Big Spatial Data Analytics. The toolkit provides different libraries:

  • Esri Geometry API for Java: A generic geometry library, can be used to extend Hadoop core with vector geometry types and operations, and enables developers to build MapReduce applications for spatial data.
  • Spatial Framework for Hadoop: Extends Hive and is based on the Esri Geometry API, to enable Hive Query Language users to leverage a set of analytical functions and geometry types. In addition to some utilities for JSON used in ArcGIS.
  • Geoprocessing Tools for Hadoop: Contains a set of ready to use ArcGIS Geoprocessing tools, based on the Esri Geometry API and Spatial Framework for Hadoop. Developers can download the source code of the tools and customize it; they can also create new tools and contribute it to the open source project. Through these tools ArcGIS users can move their spatial data and execute a pre-defined workflow inside Hadoop.

I recently learned about GeoJSON — JSON Geometry and Feature Description, but the two don’t seem to be related.

Original title and link: GIS Tools for Hadoop by Esri (NoSQL database©myNoSQL)

via: http://esri.github.com/gis-tools-for-hadoop/


Tutorial: Building Interactive Maps With Polymaps, TileStach, and MongoDB

A three part tutorial on using MongoDB, PostgreSQL/PostGIS, and Javascript libraries for building interactive maps by Hans Kuder:

  • part 1: goals and building blocks
  • part 2: geo data, PostGIS, and TileStache
  • part 3: client side and MongoDB

Original title and link: Tutorial: Building Interactive Maps With Polymaps, TileStach, and MongoDB (NoSQL database©myNoSQL)


NoSQL Frankfurt: A Quick Review of the Conference

Yesterday was the NoSQL Frankfurt conference and today we have the chance to review some of the slide decks presented.

Beyond NoSQL with MarkLogic and The Universal Index

Nuno Job (@dscape) has presented on MarkLogic — an XML server we haven’t talked too much about, its universal index, and a couple of other interesting features.

The GraphDB Landscape and sones

Achim Friedland (@ahzf) has provided a very interesting overview of the graph databases products, the goals and some scenarios for graph databases, a brief comparison of property graphs with other models (relational databases, object-oriented, semantic web/RDF, and many other interesting aspects.

Data Modeling with Cassandra Column Families

Gary Dusbabek (@gdusbabek) has covered data modeling with Cassandra (the topic I’m still finding to be one of the most complicated).

Neo4j Spatial - GIS for the rest of us

Peter Neubauer (@peterneubauer) covered another interesting topic in the data space: geographic information (GIS) in graph databases.

Even if GISers suggested this integration some time ago Neo4j announced recently support for GEO.

Cassandra vs Redis

Tim Lossen (@tlossen) slides compare Cassandra and Redis from the perspective of a Facebook game requirements. All I can say is that the conclusion is definitely interesting, but you’ll have to check the slides by yourselves.

Mastering Massive Data Volumes with Hypertable

Doug Judd — who impressed me with his fantastic Hypertable: The Ultimate Scaling Machine at the Berlin Buzzwords NoSQL conference — gave a talk on Hypertable, its architecture and performance. The presentation also mentioned two Hypertable case studies: Zvents (an analytics platform) and Reddiff.com (spam classification)[1]:

More presentations will be added as I’m receiving them.


  1. Just recently I’ve posted about Hadoop being used for spam detection.  ()

Original title and link: NoSQL Frankfurt: A Quick Review of the Conference (NoSQL databases © myNoSQL)


NoSQL Graph Databases and the Future of GIS

Coming from a GISer:

[…] I think this type of database (nb graph databases) is the obvious direction that spatial-enabled databases should take. A lot of our spatial analysis tasks involve searching the relationships between data. This could really expand those functions, and potentially make them quicker. Personally, I think this type of database is the obvious direction that spatial-enabled databases should take. A lot of our spatial analysis tasks involve searching the relationships between data. This could really expand those functions, and potentially make them quicker.

[…]

One is topology. What is topology to us but the relationship between different geometries?

[…]

The other possibility that I see with this, is relationships between metadata. Metadata in a GIS is boring. Yes it is important, but no one seems to use it, and it is tedious to create. FGDC is a pain. Metadata through relationships sounds a lot more interesting to me.

via: http://www.boxshapedworld.com/blog/post/NoSQL-the-next-step-in-GIS.aspx