Big Data Analytics: Cloud Computing

Cloud technologies and big data technologies have begun to merge and now offer a cost-effective delivery option for cloud-based big data analytics.
This post will discuss the benefits of pay-as-you-go cloud technology services provide to businesses, and organizations. Finally, this post will discuss if clouds technology will/should replace conventional data centers.

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Clouds come in three different privacy flavors: Public (all customers and companies share the all same resources), Private (only one group of clients or company can use a particular cloud resources), and Hybrid (some aspects of the cloud are public while others are private depending on the data sensitivity.

Cloud technology encompasses Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS).  These types of cloud differ in what the company managers with respect to what is managed by the cloud provider.  For IaaS the company manages the applications, data, runtime, and middleware, whereas the provider administers the O/S, virtualization, servers, storage, and networking.  For PaaS the company manages the applications, and data, whereas the vendor, administers the runtime, middleware, O/S, virtualization, servers, storage, and networking.  Finally SaaS the provider manages it all: application, data, O/S, virtualization, servers, storage, and networking (Lau, 2011).  This differs from the conventional data centers where the company managed it all: application, data, O/S, virtualization, servers, storage, and networking.

Examples of IaaS are Amazon Web Services, Rack Space, and VMware vCloud.  Examples of PaaS are Google App Engine, Windows Azure Platform, and force.com. Examples of SaaS are Gmail, Office 365, and Google Docs (Lau, 2011).

There are benefits of cloud is this pay-as-you-go business model.  One, the company can pay for as much (SaaS) or as little (IaaS) of the service that they need and how much in space they require. Two, the company can go on an On-Demand model, which businesses can scale up and down as they need (Dikaiakos, Katsaros, Mehra, Pallis, & Vakali, 2009).  For example, if a company would like a development environment for 3 weeks, they can build it up in the cloud for that time period and spend money for using the service for 3 weeks rather than buying a new set of infrastructure and setting up all the libraries.  This can help speed up the development speed in a ton of applications moving forward when you elect the cloud versus buying a new infrastructure.  These models are like renting a car.  Renting a car for what you need, but you are paying for what you use (Lau, 2011).

Replacing Conventional Data Center?

Infrastructure costs are really high.  For a company to be spending that much money on something that will get outdated in 18 months (Moore’s law of technology), it’s just a constant sink in money.  Outsourcing, infrastructure is the first step of company’s movement into the cloud.  However, companies need to understand the different privacy flavors well, because if data is stored in a public cloud, it will be hard to destroy the hardware, because you will destroy not only your data, but other people’s and company’s data.  Private clouds are best for government agencies which may need or require physical destruction of the hardware.  Government agencies may even use hybrid structures, keeping private data in the private clouds and the public stuff in a public cloud.  Companies that contract with the government could migrate to hybrid clouds in the future, and businesses without contracts with the government could go onto a public cloud.  There may always be a need to store the data on a private server, like patents, of KFC’s 7 herbs and spices recipe, but for the majority of the data, personally the cloud may be a grand place to store and work off of.

Note: Companies that do venture into moving into a cloud platform and storing data, they should focus on migrating data and data dictionaries slowly and with uniformity.  Data variables should have the same naming convention, one definition, a list of who is responsible for the data, meta-data, etc.  This would be a great chance for companies, while in migration to a new infrastructure to clean up their data.

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