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Cloud Computing
DaaS And The Public Vs. Private Debate

by John Thompson, General Manager, US Operations, www.kognitio.comMonday, September 28, 2009

If you enjoy intra-family battles, you’re going to love this one.  It combines potshots across the bow between members of the same family who, for years, were in the same “all-for-one-and-one-for-all” boat.  It also includes strong elements, present in any technology battle, pitting true believers of one side against the other.

I’m referring to cloud computing.  If you’ve done any reading over the last year, you’ve probably seen missives about the virtues of the “public” cloud versus the “private” cloud, each more passionate than the next.  Things may have come to a head earlier this year, when one vendor wrote on its corporate blog: “Private clouds are just an expensive data center with a fancy name.  We predict that 2009 will represent the rise and fall of this over-hyped concept.”  (That the vendor sells products and services that work over the public cloud would seem to be an obvious assumption.  And it would be a correct one.)

Analyst and author David Linthicum replied: “Fact is, the term 'private cloud' is a legitimate architectural pattern and option that has value within some problem domains.  Typically, private clouds are virtualized architectures that exist within private data centers but vary greatly in configuration and technology.  There is no standard for what constitutes a private cloud.”1

There is, however, a clear standard for what companies should expect from services delivered via the cloud, be it public or private.  And within the business intelligence (BI) community, that standard is quite clear.

As BI professionals, we’re accustomed to handling multiple terabytes of sensitive data.  Increasingly, companies have considered implementing BI on an outsourced basis, as a way of more rapidly analyzing and getting results from this data, at a fraction of the time and cost required by traditional data warehouse installations.  As they do so, however, the sanctity of the data is regarded as paramount.

So, while Data Warehousing as a Service (DaaS) allows a trusted provider of managed or hosted services to take over the “heavy lifting” (i.e., physical construction of the data center, network connections between the client and the center, as well as providing the analytical database and software), those advantages are not enough for some firms.  They also require a sense of rock-solid security - that the advantages of DaaS will not be overshadowed by fears of unauthorized access to their data.

This is where the concept of what we call the “private cloud” comes in, and where it has proven its value as an enabler for DaaS implementations for years.  Specifically, this entails choosing a trusted partner to handle the technical side, and not just any firm among many that may brag about its cloud uptime.  Under the concept of the “trusted cloud,” the partner can host your data and perform the needed analytics from their site, or you can allow them to access it behind your corporate firewall.  Most importantly, the firm is chosen as the preferred vendor because the end user may have an already-established relationship with the vendor, or may know a colleague who is using the firm to provide similar services, or may perceive value because the vendor has a presence in the community where the company is based.  As such, there’s a level of trust present with that type of vendor, where another, even larger, vendor may not generate the same level.

The public cloud, vendor hype notwithstanding, clearly does not at this point.  Gartner analyst Donald Feinberg was specific on his problems with warehousing data in the public cloud: “With the cloud, it could be in Bangalore, it could be in Russia or it could be in San Paulo, Brazil.  Amazon won't tell you where their machines are for security reasons.  You have no control over what machine your database is running on.  You're buying a virtual machine — that's what the cloud is — and I don't know or care where it is.”2

His inference is clear:  companies that need to know where their data is being housed, the kinds of architecture on which it’s being stored, and who has access to it do care about these very things.   GRC rules, those required in North America as well as overseas, are a key part of this focus.  They are all points where what we’ve come to know as the public cloud fall short.

So it’s in that context that mid-sized enterprises, and departments of larger firms are looking at DaaS-style implementations; they want the rapid results at a fraction of the cost.  Above all else, they want a responsive, yet accountable, analytical service that enables them to move quickly and succeed in business, while keeping the data that serves as their very lifeblood reliably secure.  It’s in that context where the private cloud, vendor pronouncements notwithstanding, succeeds.

DaaS:  the benefits

In addition to the growth of cloud computing, other new technologies have eliminated many of the constraints that historically inhibited the progress of analytic services.  A number of developments have taken place over the last three years:

  • Low-cost commodity hardware that links data warehouse costs with Moore’s Law, de-linking it from major analytical and software development costs and sharply reducing the cost to deployment.
  • The ability to deploy and re-deploy data warehouse architectures in minutes and hours, rather than days.
  • Parallel data imports that enable data loads in a fraction of the time previously required.

Couple that with the growing perceived need for DaaS at medium-sized enterprises and divisional departments where the desire to establish business value rapidly and at low cost.  Typically, these units:

  • Have data warehouses holding less than 10TB of data;
  • Need to undertake rapid, low-cost proofs of concept (POC);
  • Need results in days and weeks, not months;
  • Want operational costs amortized across a given contract period rather than upfront; and
  • Demand guaranteed service level agreement (SLA) performance.

Assuming POCs deliver the desired results, projects can move forward, with vendors handling all aspects of performance, data loading, service and systems management.

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