Our friends over at CCRi released an exciting announcement today describing their collaboration with Google on the initial release of GeoMesa for Google Cloud Bigtable, creating a vastly scalable platform for geospatial analysis that leverages the cost effectiveness and management ease of the cloud.

If you aren’t familiar with GeoMesa, it’s an open-source extension that quickly stores, indexes, and queries hundreds of billions of geospatial features in a distributed database built on Apache Accumulo.  GeoMesa leverages GeoServer for its spatial processing, and we’ve been working with CCRi for a while to combine the data management and publishing capabilities of OpenGeo Suite with the big data analytics capabilities of GeoMesa.

At the same time, Google today announced Google Cloud Bigtable; a fully managed, high-performance, extremely scalable NoSQL database service accessible through the industry-standard, open-source Apache HBase API. Under the hood this new service is powered by Bigtable, the same database that drives nearly all of Google’s largest applications.

CCRi’s announcement means that GeoMesa is now supported on Google Cloud Bigtable. As noted in CCRi’s blog post, when using Google Cloud Bigtable to back GeoMesa, developers and IT professionals are freed from the need to stand up and maintain complex cloud computing environments. These environments are not only expensive to build, but they require highly-trained DevOps Engineers to maintain them and grow them as the data accumulates.  Because GeoMesa supports Open Geospatial Consortium (OGC) standards, developers can easily migrate existing systems or build new systems on top of GeoMesa. Developers familiar with GeoServer or the OpenGeo Suite can use the GeoMesa plugin to add new data stores backed by Google Cloud Bigtable.

Let’s think for a moment about the opportunity here.  As an industry, organizations like CCRi are continuing to advance how spatial processing can be applied to big data (NoSQL, key-value pair, graph) stores, and GeoMesa is a great example of this.  I have also seen examples of OpenGeo Suite spatially enabling content in a speed layer of a Lambda architecture leveraging Apache Spark or Apache Storm.  And while these advancements do illustrate value added, the infrastructure and knowledge needed to setup these architectures is not trivial. Leveraging capabilities like GeoMesa for Google Cloud Bigtable makes geospatial analytics with big data accessible to a much wider audience.