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Jun 20, 2013

Data in motion vs. data at rest

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Data in Motion vs. Data at RestGaining insights from big data is no small task. Having the right technology in place to collect, manage and analyze data for predictive purposes or real-time insight is critical. Different types of data may require different computing platforms to provide meaningful insights. Understanding the difference between data in motion vs. data at rest can help determine the type of technology and processing capabilities required to glean insights from the data.

Data at rest
This refers to data that has been collected from various sources and is then analyzed after the event occurs. The point where the data is analyzed and the point where action is taken on it occur at two separate times. For example, a retailer analyzes a previous month’s sales data and uses it to make strategic decisions about the present month’s business activities. The action takes place after the data-creating event has occurred. This data is meaningful to the retailer, and allows them to create marketing campaigns and send customized coupons based on customer purchasing behavior and other variables. While the data provides value, the business impact is dependent on the customer coming back in the store to take advantage of the offers.

Data in motion
The collection process for data in motion is similar to that of data at rest; however, the difference lies in the analytics. In this case, the analytics occur in real-time as the event happens. An example here would be a theme park that uses wristbands to collect data about their guests. These wristbands would constantly record data about the guest’s activities, and the park could use this information to personalize the guest visit with special surprises or suggested activities based on their behavior. This allows the business to customize the guest experience during the visit. Organizations have a tremendous opportunity to improve business results in these scenarios.

Infrastructure for data processing
You might be wondering what type of IT Infrastructure would be needed to support data processing for both of these types. The answer depends on which method you choose, and your business objectives for the data.

For data at rest, a batch processing method would be most likely. In this case, you could spin up a bare-metal server during the time you need to analyze the data and shut it back down when you are done. With no need for “always on” infrastructure, this approach provides access to high-performance processing capabilities as needed.

For data in motion, you’d want to utilize a real-time processing method. In this case, latency becomes a key consideration because a lag in processing could result in a missed opportunity to improve business results. By eliminating the resource constraints of multi-tenancy, bare-metal cloud offers reduced latency and high performance levels, making it a good choice for processing large volumes of high-velocity data in real time.

Both types of data have their advantages, and can provide meaningful insights for your business. Determining the right processing method and infrastructure depends on the requirements for your specific use case and data strategy.

Learn more about the benefits of bare-metal cloud for different types of big data workloads.

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May 1, 2013

Meet the challenges of big data infrastructure with colocation

INAP

big data infrastructure with colocationTo create a cost-effective infrastructure option for housing big data deployments, many IT organizations have been forced to use a combination of cloud, dedicated hosting and colocation from multiple providers. But the emergence of new colocation services that provide cloud-like flexibility along with secure physical infrastructure helps meet the demands of big data through data center hybridization.

Today, the largest hard drive you can buy provides 3 terabytes of storage. Online gamers playing Battlefield 3 will generate enough data to fill that entire drive in just three days. Twitter generates enough information to fill one up in a mere six hours. Facebook is even more impressive: they collect 500 terabytes of data per day, which means it would take them a scant 8 minutes to fill up the world’s largest hard drive.

So where do companies actually put all of that data? If you’re Facebook, Twitter, Google or one of a handful of other industry titans, you build your own data centers and fill them with servers as fast as you can build them. But if you’re not yet a multi-billion dollar corporation, building your own state-of-the-art data center is probably not a viable option. Cloud and dedicated hosting can provide flexible contracts and tools that make hosting and scaling traditional web applications easier, but these options make less sense when you start talking about storing petabytes of big data.

The evolution of colocation
Many companies find that the most cost-effective way of deploying a big data cluster is to put it in someone else’s state-of-the-art data center via colocation. But cost-effectiveness is only part of the equation. Traditionally, colocation hasn’t been able to offer the customizable options of dedicated hosting, or the agility and flexibility of cloud. Whether it’s trying to meet additional seasonal demand, running an intensive one-off report on your big data deployment, or deploying a new web application, companies can’t wait four weeks to deploy it in a collocated environment. They need access to on-demand resources.

Hybridization
The ability to connect your colocation and cloud resources can fill this void, and help bridge the gap between big data deployment and the cloud. Internap’s Platform Connect offers true hybridization and allows you to seamlessly link your colocation, Custom and Agile hosting, and AgileCLOUD environments across the same layer 2 or layer 3 network within any of our Agile-enabled facilities. Launching your new free-to-play game next month and need to make sure your MongoDB deployment can temporarily handle a 500% increase in the amount of data you’re collecting? Need to run a series of reports on your CouchDB deployment that’s already seeing 85% utilization? Want to replicate your 80TB Hadoop deployment for a few weeks so you can see firsthand what effects an hdfs changes will have on your queries? No problem.

Multiple services, one provider
Instead of searching for multiple providers – one partner for colocation and IP, one for dedicated hosting and one for cloud – Internap offers multiple services under one roof, including award-winning colocation for housing your big data, the industry’s fastest and most reliable IP , exceptional Custom and Agile hosting services for your websites and mission critical applications and the fastest and most cost-effective cloud for your on demand needs. Being able to reach out to a single provider who understands your IT needs at every layer of the OSI model is a small but important step.

Until now, traditional colocation has taken a backseat to dedicated hosting and cloud computing, but it’s emerging as an agile, cost-effective option for housing big data. The ability to hybridize your data center with Platform Connect and apply cloud-like flexibility to physical servers offers IT organizations a best of both worlds approach to big data deployments. (See these capabilities in action at Internap’s New York Metro data center.)

Learn more about how Platform Connect can hybridize your data center and provide a new approach to big data infrastructure challenges.

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