Using Cloud Computing for Big Data Management
December 6, 2013
So you’ve probably heard about how the cloud can be leveraged for managing big data, but how exactly can you do that? Well, there are a variety of service options available in order to achieve many business goals that you may not be able to accomplish practically with other solutions. Managing big data in the cloud ultimately makes it easier to analyze and creates an environment that supports better decision making. Below is a breakdown of what exactly cloud computing is, how the cloud can be used for big data management and what you can expect as you move your organization to the cloud.
Analysis, Protection and Savings: Data Benefits from the Cloud
As you begin implementing a cloud environment within your organization, understand that every company uses a different process for gathering and analyzing large volumes of data within the cloud. One of the most impressive benefits of the cloud is its ability to effectively manage the sheer volume of data it holds, but the idea is to find the method which works the most efficiently and effectively for your specific organization. Once your ideal process is uncovered, cloud computing allows for the analysis of large amounts of structured and unstructured data in order to derive the most value from it. Additionally, your analysis technique can potentially evolve as your business advances, making it more effective than traditional predictive models. The data types housed in a cloud environment can also be protected for long periods at a time within the cloud.
The idea of data protection has become the driving force behind many companies’ decisions to migrate to the cloud. The cloud has rapidly evolved into an easy to use platform for disaster recovery plans. If data is stored within a cloud environment when disaster strikes, companies can ensure the safety of their records and, more importantly, they are capable of resuming business as normal quickly following the event. By utilizing the cloud for data storage, companies can easily retrieve unstructured data including emails, video, images and social media, as well as their structured data that is comprised of credit card transactions and customer contact information.
For many businesses the motive for adopting the cloud is the cost savings that they will incur, predominantly if they utilize a cloud service provider. This is true to an extent, over time data management costs will decrease, however, as with any new technology there will be significant learning costs incurred first. That is not to say that transitioning to the cloud will not pay off, in fact a number of organizations witness upwards of a $30 return on every $1 invested into the platform. The opportunity for success is there so long as you don’t go into it blindly thinking that you will begin to see the return right away and if you practice smart investing into the specific cloud services themselves.
Analytics as a Service (AaaS) – Data Management in the Cloud
When using cloud-based services for big data management the term often used to define the broad analysis process is Analytics as a Service (AaaS). Any effective cloud-based AaaS framework will encompass six analytics elements, including:
- Data Sources
- Data Models
- Processing Applications
- Computing Power
- Analytic Models
- Sharing or Storing Results
All six keys must be covered in order to begin deriving the profitable intelligence you want out of big data management. However, many providers who only cover one or two of the above elements will still call themselves cloud-based analytics providers. This is one of those areas that can cost you greatly if you fail to plan properly, so ensure that all of these elements will be covered by whichever provider you choose before investing your budget into them.
If this wasn’t confusing enough, AaaS can be deployed in the cloud through any one of the three cloud service types:
- Infrastructure as a Service (IaaS)
- Platform as a Service (PaaS)
- Software as a Service (SaaS)
Your organization’s specific goals and the cloud environment, whether on-premise or through a cloud provider, will determine the particular service mix utilized for data management and analysis. The thing with cloud services is that you can use just one, all or a combination of the service types, whatever works best for you. Extensive research and thought should be given before choosing which service to use and then the specific service vendor that will deliver it. This is a major area where costs can rise exponentially if not planned for properly, so planning should never be skimped on in this area or your ROI will inevitably be delayed.
Infrastructure as a Service (IaaS)
IaaS is deployed through a cloud provider as a way to provide a strong foundation for a company’s cloud services. Through this model, an organization outsources the operations equipment; however, it will require a greater investment of other resources from the purchasing agent in order to fully implement big data analytics onto the IaaS foundation. This service type requires the organization utilizing it to still install the actual analytics software, such as the popular Hadoop Framework, or a NoSQL database of their choice. The analytics team will also be expected to manage the resources being utilized through this service but this can be automated through various management tools in order to run more smoothly.
IaaS’s main advantage is the fact that the by managing the infrastructure, a service provider can deliver tools for faster automation and computing. Speed plays a huge role in data analytics especially in terms of inventory management or any type of analysis that relies heavily on the timeliness of the material. By outsourcing the storage and processing capabilities, organizations, especially smaller ones, can still perform detailed analyses without actually purchasing the advanced computing tools they need.
Popular IaaS Vendors include:
- Amazon Web Services
Platform as a Service (PaaS)
PaaS allows organizations to rent virtualized servers and services in order to run existing applications as well as for application development and testing purposes. The benefit of this service is that geographically dispersed teams can easily work together through it and features can be easily changed and upgraded as needed. The development aspect of PaaS also allows for advanced data analytics applications to be customized to an organization’s specific needs, developed and then run through the service.
Popular PaaS Vendors include:
- Google Apps
- Windows Azure
Software as a Service (SaaS)
SaaS allows customers to access particular ready-made applications over a network. This often requires organizations to use multiple applications in order to cover all of the necessary business areas for effective data management. Like most already prepared applications, administration is easier, compatibility and collaboration opportunities are better and they are updated and patched automatically. However, the downside is they can’t be altered as through a PaaS in order to conform better with your specific business and data analytics needs.
Popular SaaS Vendors include:
Which Cloud is the Best?
While organizations can elect to use just one cloud service these tools are so different that they can actually perform best when used together in order to cover all the key analysis elements. Smaller organizations benefit the most from using these various cloud service providers to purchase software applications and rent the virtualized servers and computing power they need. In this way, it provides access to the latest technology available for rapid computing, collaboration and storage for only a fraction of the cost. Since these organizations are small they only need to rent minuscule space on a server and can increase or decrease as needed in order to perform better and also reduce any unnecessary costs.
Larger organizations can also utilize these tools but they also need to consider how much equipment they are outsourcing and if it would be more cost efficient to build their own infrastructures and platforms. There is a lot of choice and flexibility in the cloud, which makes it certain that any organization can find the solutions they need but without proper planning, the trial and error of finding the right solutions can really cost a company.
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