Big data is creating a massive disruption for the IT industry. Faced with
exponentially growing data volumes in every area of business and the web,
companies around the world are looking beyond their current databases and
data warehouses for new ways to handle this data deluge.
Taking a lead from Google, a number of organizations have been exploring the
potential of MapReduce, and its open source clone Hadoop, for big data
processing. The MapReduce/Hadoop approach is based around the idea that
what's needed is not database processing with SQL queries, but rather
dataflow computing with simple parallel programming primitives such as map
As Google and others have shown, this kind of basic dataflow programming
model can be implemented as a coarse-grain set of parallel tasks that can be
run across hundreds or thousands of machines, to carry out large-scale bat... (more)
Cloudcel on Ulitzer
At the recent Hadoop World conference, Doug Cutting, Hadoop Project Founder,
remarked that "The Dream" was to provide non-programmers with the power of
parallel cloud computing tools such as MapReduce and Hadoop, via simple,
easy-to-use spreadsheet-like interfaces.
With Cloudcel, the non-programmers of the world (and the programmers too!)
can "live that dream" today.
Not only can you develop and launch massively parallel MapReduce/Hadoop-style
cloud computations, simply and seamlessly from within the standard Excel
interface, you can also go way beyond tools ... (more)
Bill McColl's "Cloud N" Blog
This is an incredibly important time for the cloud computing area. But
let’s try and move the discussion of it in the press along from an
obsession with new datacenter buildings located by power stations, with the
total server numbers at Microsoft and Google, and with Amazon’s hourly
pricing for EC2. Interesting though those aspects of cloud computing appear
to be to journalists, they hardly represent what is really industry changing
about cloud computing.
What are some of the new directions in the massively parallel cloud computing
space? I’ll mentio... (more)
Cloud Data Analytics on Ulitzer
In the previous article we looked at how realtime cloud analytics looks set
to disrupt the $25B SQL/OLAP sector of the IT industry. What are users
looking for from a next-generation post-SQL/OLAP enterprise analytics
solution? Let's look at the requirements:
Realtime + Historical Data. In addition to analyzing (historical) data held
in databases (Oracle, SQLServer, DB2, MySQL) or datastores (Hadoop, Amazon
Elastic MapReduce), a next-gen analytics solution needs to be able to
analyze, filter and transform live data streams in realtime, with low
For twenty years, analytics has been viewed as just one specific area within
the broader relational database industry. So, analytics has meant databases.
Today that view is changing. Over the past year or so, a new movement, the
"NoSQL" movement has emerged promoting the advantages of doing a variety of
kinds of analytics without using any relational database technologies at all.
Whatever one thinks of the capabilities and limitations of distributed
key-value stores relative to relational databases, one thing is clear - the
stranglehold that SQL has held over all aspects of data an... (more)