First of all, it's beautifully engineered, as long as you just need a KV store or a graph DB (I wasn't in love with the MapReduce stuff, but that's another story). None of the hassle that Hadoop/Hbase have about some nodes being special (HBase Master, HDFS Namenode, etc). Also, no running multiple daemons per server (e.g., no distinction between regionserver and datanode daemons, like HBase). Easy config files, simple HTTP API (so you can just throw an off the shelf load balancer like HAProxy in front of it), and lots of little things that just make life easier.
I also really like how it's very explicit about the CAP tradeoffs it makes - with powerful conflict resolution available for when consistency has been sacrificed (instead of trying to sweep the issue under the rug, like many other distributed dbs do).
However, there are a few downsides.
First, as mentioned in the article, with the default backend (a kv-store called 'bitcask') all the keys per node have to fit in memory (and each key has, on the order of, 20 bits of overhead, IIRC). Annoyingly, this fact isn't noted anywhere in the Riak documentation that I saw (although, there is a work-around by using the InnoDB backed). This won't matter too much for many use cases, but it can be pretty painful if you're not expecting it.
Second, you can guard against data loss by specifying N copies of each piece of data are stored on your cluster. However, under some conditions (https://issues.basho.com/show_bug.cgi?id=228), the data may be replicated on less than N distinct nodes. So, the failure of less than N nodes could result in dataloss.
Finally, to the best of my knowledge, the largest Riak clusters in production are around 60 nodes, while Hadoop has 2000 node clusters running (e.g., FB) in production. Perfectly acceptable for most users, but just one more thing to worry about if you're planning to roll out a large cluster (more potential for 'unknown unknowns', so to speak).