CIOReview
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BIG DATA SPECIALJULY 19, 2016

CIOREVIEW.COM

100 Most Promising Big Data Solution Providers 2016

‘Big Data’ is no more a buzzword. Now thatorganizations have already put their widearms around Big Data, the next impedimentlies in refining the data to bring out insightful
and meaningful results. With every bit of ‘0’ and ‘1’ being
harnessed as meaningful “collections,” enterprises are sureto achieve complete utilization of their concrete information,
and attain significant outcomes. Another trend that has
gained notable traction is capitalization of cloud for storing
invaluable sets of data. While majority of enterprises
consider it as a risky venture owing to the concerns ofsecurity, others embark upon it for the sake of businessagility. With myriad of such transformations, enterprises are
confident to embrace innovative ways to hold together theregulations of corporate world, and ensure their participation

Rasdamanrecognized by|108|

JULY 2014

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Website:Rasdamanmagazine as

An annual listing of 100 companies that are at the forefront of providing
bigdata solutions and impacting the marketplace

CIOReviewin the realm of Big Data. In order to uphold a fine balance, it
has become critical for the CIOs to choose proper technology
and select best vendors that are at the forefront of effectively
tacking the impediments across the Big Data realm. To
help them accomplish their objective, CIO Review presents“100 Most Promising Big Data Solution Providers 2016.” A
distinguished panel comprising of CEOs, CIOs, VCs, andanalysts including CIO Review’s editorial board has decided
the top Big Data Solution Providers from over thousandcompanies. The companies featured in this list provide alook into how their products work in the real world, so thatyou can gain a comprehensive understanding of the solutions
available and how they stand against competition. We present
to you CIO Review’s 100 Most Promising Big Data Solution
Providers 2016.

Peter Baumann,
CEO

Agile analytics on massive
multidimensional arrays such as sensor,
image, simulation, and statistics data

rasdaman.com

rasdaman
Pioneering Agile Array Analytics

In our quest for disruptive big datatechnology we crossed the Atlantic
to find German-based rasdaman,
which truly has the potential forbecoming a game changer right tomorrow.
Turns out the Caribbean sounding nameactually stands for “raster data manager”.
Peter Baumann, inventor CEO of thecompany, early on has recognized a gap
in big data analytics: missing support
for massive matrices and datacubes, also
known as multi-dimensional arrays. We
find these arrays everywhere: in business,such as stock risk analysis and OLAP;
in Life Science; in exploration data; andin industrial simulation. Even analyzinglarge graphs, like the Facebook One,can be done through array operations.
In fact, datacubes are recognized as a
key paradigm that is simple to grasp andeasy to query. As we see the potential forarray engines, we wonder: How could thatgo undetected?
“SQL has missed a train,” reveals
Baumann, “its designers didn’t like what
didn’t fit table world. NoSQL ultimately isa response to this, and array engines arepart of it.” Left alone, data centers andcompanies created homegrown solutions
which typically ended up in silo solutions
with a rapidly eroding architecture.
Rasdaman, conversely, started with ageneric, clean slate architecture. The coreidea is as simple as compelling: combine
the flexibility of SQL with the power ofarray manipulation.
“Good old SQL is a great tool with its
flexibility of ‘any query, anytime’ – justnot on datacubes,” explains Baumann.
Constructed as a NoSQL database,rasdaman connects itself with standard
SQL and extends it with array operations.
This opens up additional vistas: suddenly
the big, clumsy data and the small, smart
metadata can be queried together. Closing

this age-old gap liberates users fromkeeping in mind different access andretrieval techniques – a single common
information space emerges.
rasdaman can handle really large
volumes quickly, and can combine data
sources distributed on planetary scale.
We found installations exceeding 130
Terabyte at publicly accessible services,and researchers at superscale data centers,
like the European Space Agency (ESA)
and the European Centre for MediumRange Weather Forecast (ECMWF)
with its 87 Petabyte climate archive,
are feeding rasdaman to go beyond thePetabyte frontier.

With rasdaman’s uniqueadaptive data partitioning andparallelization, data cubes
are analyzed and combinedin a straightforward andultrafast manner
How rasdaman does this job? “The
whole architecture is crafted from scratch,
optimized for array handling,” outlines
Baumann. There is a series of strong
optimizations which altogether makerasdaman fast. Adaptive data partitioning
and distribution is one element, augmented
with effective compression on dense andsparse datacubes; intelligent processing
utilizes all silicon it can get hold of, within
and across nodes and even data centers while
respecting security. “Query optimizationand parallelization is done individually foreach incoming query, as opposed to static
parallelization like in Spark,” describes
Baumann. And confronts us with a demo
where we see a Terabyte analyzed in less than
100 milliseconds.

Peter Baumann
And how do data get in? Ingestion
pipelines are configured rapidly – oryou avoid copying at all, as rasdamancan be adjusted to tap directly into any
preexisting archive structure. “Thisallows us to be uniquely customer
oriented and fast while accommodating
any change request swiftly,” says
Baumann. “Customers deploy rasdaman
as a cost-effective solution for valueadding services, essentially enablingusers to build their own data product on
the go,” he adds.
This lead got rasdaman on the radar ofstandardization bodies. ISO is finalizing
an SQL extension called MDA (for MultiDimensional Arrays) – using rasdaman
as its blueprint. “In 2017, ISO SQL/MDA
will become the standard for large-scale
array analytics, and rasdaman will be
the first fully scalable implementation,”notes Baumann.
“The net effect is a tremendous
boost in productivity of analysts,scientists, and engineers, achievedon commodity hardware and clouds,”
concludes Baumann. We follow this,and wholeheartedly include this pickin our list of 100 Most Promising Big
Data technologies.

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