When we last left Oracle’s big data plans, there was definitely a missing
piece. Oracle’s Big Data Appliance as initially disclosed at last fall’s
OpenWorld was a vague plan that appeared to be positioned primarily as an
appliance that would accompany and feed data to Exadata. Oracle did specify
some utilities, such as an enterprise version of the open source R
statistical processing program that was designed for multithreaded execution,
plus a distribution of a NoSQL database based on Oracle’s BerkeleyDB as an
alternative to Apache Hive. But the emphasis appeared to be extraction and
transformation of data for Exadata via Oracle’s own utilities that were
optimized for its platform.
With Oracle’s announcement of general availability of the big data
appliance, it is filling in the blanks.
As such, Oracle’s plan for Hadoop was competition, not for Cloudera (or
Of the 3 "V’s” of Big Data – volume, variety, velocity (we’d add
"Value” as the 4th V) – velocity has been the unsung ‘V.’ With the
spotlight on Hadoop, the popular image of Big Data is large petabyte data
stores of unstructured data (which are the first two V’s). While Big Data
has been thought of as large stores of data at rest, it can also be about
data in motion.
"Fast Data” refers to processes that require lower latencies than would
otherwise be possible with optimized disk-based storage. Fast Data is not a
single technology, but a spectrum of approaches that process data t... (more)
At this point, probably at least 90 percent or more of analytic systems/data
warehouses are easily contained within the SQL-based technologies that are
commercially available today. We’ll take that argument a step further: Most
enterprise data warehouses are less than 5 terabytes. So why then all the
excitement about big data, and why are acquisitions in this field becoming
almost a biweekly thing?
To refresh the memory, barely a couple weeks back, HP announced its intention
to buy Vertica. And this morning came the news that Teradata is buying the
other 89 percent of Aster Data ... (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,
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)