To date, Big Storage has been locked out of Big Data. It’s been all about
direct attached storage for several reasons. First, Advanced SQL players have
typically optimized architectures from data structure (using columnar),
unique compression algorithms, and liberal usage of caching to juice response
over hundreds of terabytes. For the NoSQL side, it’s been about cheap,
cheap, cheap along the Internet data center model: have lots of commodity
stuff and scale it out. Hadoop was engineered exactly for such an
architecture; rather than speed, it was optimized for sheer linear scale.
Over the past year, most of the major platform players have planted their
table stakes with Hadoop. Not surprisingly, IT household names are seeking to
somehow tame Hadoop and make it safe for the enterprise.
Up ' til now, anybody with armies of the best software engineers that
Internet fir... (more)
In its rise to leadership of the ERP market, SAP shrewdly placed bounds
around its strategy: it would stick to its knitting on applications and rely
on partnerships with systems integrators to get critical mass implementation
across the Global 2000. When it came to architecture, SAP left no doubt of
its ambitions to own the application tier, while leaving the data tier to the
kindness of strangers (or in Oracle’s case, the estranged).
Times change in more ways than one – and one of those ways is in the data
tier. The headlines of SAP acquiring Sybase (for its mobile assets,
HP chose the occasion of its Q3 earnings call to drop the bomb. The company
that under Mike Hurd’s watch focused on Converged Infrastructure, spending
almost $7 billion to buy Palm, 3COM, and 3PAR, is now pulling a 180 in
ditching both the PC and Palm hardware business, and making an offer to buy
Autonomy, one of the last major independent enterprise content management
players, for roughly $11 billion.
At first glance, the deal makes perfect sense, given Leo Apotheker’s
enterprise software orientation. From that standpoint, Apotheker has made
some shrewd moves, putting aging ent... (more)
It’s no secret that rocket .. err … data scientists are in short supply.
The explosion of data and the corresponding explosion of tools, and the
knock-on impacts of Moore’s and Metcalfe’s laws, is that there is more
data, more connections, and more technology to process it than ever. At last
year’s Hadoop World, there was a feeding frenzy for data scientists, which
only barely dwarfed demand for the more technically oriented data architects.
In English, that means:
Potential MacArthur Grant recipients who have a passion and insight for data,
the mathematical and statistical prow... (more)
Hadoop remains a difficult platform for most enterprises to master. For now
skills are still hard to come by – both for data architect or engineer, and
especially for data scientists. It still takes too much skill, tape, and
baling wire to get a Hadoop cluster together. Not every enterprise is Google
or Facebook, with armies of software engineers that they can throw at a
problem. With some exceptions, most enterprises don’t deal with data on the
scale of Google or Facebook either – but the bar is rising.
If 2011 was the year that the big IT data warehouse and analytic platform