Back from Hiatus - Summary Update 1
KickFireI think Kickfire has been doing it a little tough lately. The difficulties in a startup launching a hardware appliance (and associated logistics) combined with being too focused on the MySQL customer base has impacted the growth of this interesting start up. But they aren’t taking it lying down and have adjusted the strategy and have added a new appliance to the range. Kickfire now seems to have a stronger focus on the enterprise and has released a larger version of its appliance to provide a growth path. As I have said all along, the MySQL aspect of their product is interesting but the solution as a whole is much more interesting and has much broader appeal than just the current MySQL customer base.
Flipping hardware appliances is a much tougher play than software only solutions, partly due to it being much more difficult for customers to get their hands on your stuff and have a play before they buy. Hopefully Kickfire has mitigated most of these issues now though their online, on demand evaluation host. I haven’t yet played with this but it is on my list of things to do over the coming month.
Kickfire’s enterprise strategy is just one of many that will be re-enforced by an Oracle acquisition of Sun.
GreenplumGreenplum has addressed a perceived chink in its amour with the release of its column store capability. Greenplum has taken the popular hybrid approach which means on a case by case basis you can decide if a particular table should be row or column orientated. But as Daniel points out, it is a storage level only solution. The storage only approach brings just part of the benefit of columnar stores, to achieve the full benefit the query execution engine needs to be aware of this layout (so features such as lightweight compression can be effectively used). But I am sure this is an area where Greenplum will make further improvements in the future.
GroovyGroovy has been working hard carving out its niche in the real time web data market. If you don’t recall, Groovy makes an in-memory RDBMS that has been extended to provide real time data streaming capabilities. Groovy has been positioning this into the large web properties who are working on creating new large scale, real time applications for their user base.
Aster DataAster has put out a number of announcements over the last month and I am trying to keep up. Firstly they announced their tight integration with Hadoop. This integration with Hadoop is map-reduce on the outside of the Aster Data platform (which apparently they didn’t have already although I think everyone assumed they did given their strong in database map-reduce message). Aster has been banging the map-reduce drum for some time and is clearly the point of difference they are focusing on.
Aster has also release version 4.0 of their platform a couple of days ago, then a few days ago I was a bit surprised to see an email from them referring to their platform as “the World's First Massively Parallel Data-Application Server”. This seems to be a new name reference to the in database map-reduce stuff, maybe as an effort to differentiate themselves from the myriad of competitors in this space they are trying to carve out a new category all for themselves. For me, the external map-reduce stuff makes sense as I can see this being useful for data preparation on the way in to Aster and data dissemination of data on its way out of Aster. But I still don’t have in my head clear examples when their in database map-reduce stuff is useful. I am sure it is but I have a feeling it is valuable on a case by case basis which is difficult to articulate especially as a point of difference message. But I missed Curt’s map-reduce webinar (at the last minute) so maybe that would have shed some light. Anyway, they are running a webinar on this which you can register for here.
To me, Aster is more aggressively driving their platform into green fields trying to leverage their technology to find new customers and new markets. Greenplum on the other hand is more ‘steady as she goes’, focusing on a more traditional and conservative enterprise data warehousing market (while still innovating ahead of the general purpose behemoth's). The risks are on both sides. When trying to define a new market you risk not finding one or finding one that is too small or “niche” to support your business. With the conservative approach you risk being lumped in with everyone else, and in data warehousing ‘everyone else’ is now quite a long list.