Big Data Platforms as a Service (PaaS) lets an organization take advantage of a service providers compute power, analytical tools, store as much data as needed and pay only for resources used. Data should be protected with multiple layers of security, replicated across multiple data centers and easily exported.

The real value of Big Data Platforms as a Service is the ability to quickly scale-up big data projects without the upfront CapEx required for an on-premise deployment. Additionally, organizations can scale down fast and pay only for the storage and compute resources they use.

Big Data PaaS offerings reduce the need for organizations to hire and/or train big data staff, a challenging task considering a lack of skilled big data practitioners at this time. Rather, the service providers are responsible for deploying, managing and scaling installations.

Big Data PaaS may be a good potential starting point for organizations looking to tap into the power of big data analytics but are not prepared to commit to a full-scale, production level deployment at this time. 

We are in the pre-industrial age of data analytical platforms and there are many different types of Big Data PaaS offerings. The following is a partial list:

  • Google BigQuery
  • Microsoft Azure Hadoop
  • Qubole
  • BigML
  • Bitdeli
  • Google Prediction API
  • KXEN
  • Infochimps
  • Kontagent
  • Mortar Data
  • Placed Analytics
  • Precog
  • Continuuity
  • Spring for Hadoop
  • Statsmix

See: http://bit.ly/X0IcSy

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Tags: Big, Data, PaaS, Platform, Service, a, as


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Comment by Cal Garrett on January 8, 2013 at 4:50am

There are other vendors providing analytics for big data that are worth exploring and have been around longer than most of the vendors on your list.  1010data for example is a Analytics Platform as a Service and espouses similar benefits for lower TCO and rapid time to deploy.  The operation runs entirely as a private hosted service as opposed to a web-based development platform.  Customer data often larger than 2 billion rows a day are loaded in minutes. The database and the analytics are on the same platform so data is not piped back and forth as in the traditional Enterprise Data Warehouse approach with ETL/BI/and Predictive Modeling software tools bolted on causing tools and report and data mart proliferation.  Query times on 10s billion row tables and highly complex algorithms achieve query run rates that are impossible for large iron infrastructures to achieve. The columnar nature of the database, the proprietary and extremely elegant command language and MPP server cluster make this possible.  And the GUI is web-based so requires no software other than a standard web browser to access the data.  And of course the data center is state of the art with all the expected security protocols and backup.  10 years old, profitable, 200+ customers, one in retail has 10,000 locations. That's big data.  Worth a look.  www.1010data.com

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