The geographic distribution of NoSQL skills: HBase and Membase

Following last week’s post putting the geographic distribution of Hadoop skills, based on a search of LinkedIn members, in context, this week we will be publishing a series of posts looking in detail at the various NoSQL projects.

The posts examine the geographic spread of LinkedIn members citing a specific NoSQL database in their member profiles, as of December 1, and provides an interesting illustration of the state of adoption for each.

We begin this week’s series with Membase and HBase, the two projects that proved, like Apache Hadoop, to have significantly greater adoption in the USA compared to the rest of the world.

The statistics showed that 58.2% of the 170 LinkedIn members with “Membase” in their member profiles are based in the US (as previously explained, we tried the same search with Couchbase, but with only 85 results we decided to use the Membase result set as it was more statistically relevant).

As with Hadoop, a significant proportion (27.1%) of those are in the Bay area, the highest proportion of all the NoSQL databases we looked at. The results also indicate that Ukraine is a hot-spot for Membase skills, with 3.5%, while Membase adoption is lower the UK (2.4%) than other NoSQL databases.

It should not be a great surprise that Apache HBase returned similar results to Apache Hadoop. The top eight individual regions for HBase were exactly the same as for Hadoop, although the UK (3.4%) is stronger for HBase, as is India (10.7%).

The statistics showed that 57.0% of the 1,687 LinkedIn members with “HBase” in their member profiles are based in the US, with 25.0% in the Bay area (the third highest in our sample behind Hadoop and Membase).

The series will continue later this week with MongoDB, Riak, CouchDB, Apache Cassandra, Neo4j, and Redis.

N.B. The size of the boxes is in proportion to the search result (click each image for a larger version). World map image: Owen Blacker