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Archive for the ‘Big Data’ Category

 

High RAM Cloud Servers for Distributed Caching

Tuesday, June 10th, 2014 by

GoGrid has just released High RAM Cloud Servers on our high-performance fabric. These servers are designed to provide a high amount of available RAM that is most commonly required for caching servers. Like our other recent product releases, these servers are all built on our redundant 10-Gbps public and private network.

High RAM Cloud Servers are available in the following configurations:

High RAM RAM Cores SSD Storage
X-Large 16 GB 4 40 GB
2X-Large 32 GB 8 40 GB
4X-Large 64 GB 16 40 GB
8X-Large 128 GB 28 40 GB
16X-Large 256 GB 40 40 GB

 

 

 

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How does Big Data fit into marketing?

Tuesday, May 27th, 2014 by

Even midsize businesses looking to cast a wider net are integrating Big Data into their marketing strategies. Make no mistake: Human insight will never become obsolete in the face of analytical marketing. An organization can have the most advanced analysis program on the planet, but if those reviewing the information can’t make heads or tails of it, then there’s no point in using the system.

Diagram of a brand promotion strategy.

Diagram of a brand promotion strategy.

Possessing a robust Marking operation goes far beyond searching for the latest and greatest analysis platform. Although it may lead to success, Marketing isn’t the be-all, end-all solution to every problem. Making the most out of any system is a two-way street: a company’s human assets must regard it as a technological assistant and support it with the appropriate environment.

Move into the cloud
To receive thorough, well-detailed reports, organizations want to be able to aggregate as much digital information as possible. Instead of cramming all  this data onto predefined, legacy platforms, professionals should strongly consider investing in cloud computing. When enterprises decide to move toward remote access, concerns like overworking a system, general server maintenance, and load-balancing are eliminated. The scalable environments can be accessed from almost anywhere, enabling marketers to easily obtain files stored on cloud servers and make decisions wherever they are.

Provide insights
Once an adequate support system has been established, CMOs can begin launching analytics programs to figure out how customers consistently interact with their brand through multiple channels. The question is, how do companies manage such a relentless flow of data? Jason Bowden, a contributor to Business 2 Community, claimed that it all depends on the company’s angle. Gaining insight from a large amount of intelligence doesn’t need to involve feeding it to an unwieldy, self-automated machine in the hope that actionable insights will come out the other end.

Instead, marketers should set clearly defined goals. Do they want to know why a certain product on an e-commerce site isn’t receiving hits? Are they trying to determine how in-store item placement affects customer decisions? These are just two of the many scenarios they may face. Bowman acknowledged a few ways to handle data appropriately:

  • Percolate the information and identify which aspects of a digital marketing campaign can generate greater leads.
  • Filter applicable metrics that will display practical ways to reinforce products and services to entice consumers to invest.
  • Leverage data to create a pattern of how to chart weaknesses, enabling employees to pinpoint the source of issues.

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Farmers Use Big Data to Improve Crop Yields

Wednesday, May 14th, 2014 by

For the past few years, scientists throughout the world have referenced an impending food shortage of global proportions. The prospect of feeding 9 billion people in the year 2050 is intimidating, motivating organizations to turn to advanced technology. If harnessed properly, Big Data could help agriculturalists and food companies find ways to supply a world population that’s increasing dramatically.

A farmer reaps his wheat crop.

A farmer reaps his wheat crop.

Moving into the 21st century 
When the farming industry comes to mind, people often think of an archaic, anachronistic practice that lags behind when it comes to technological progression. Although every other sector seems to be adopting cloud computing, advanced software solutions, and analytics programs, agriculture appears to have been left in the dust.

Even though such a perception may be widespread, there’s no denying the sector’s importance: “No farms, no food” is the way numerous bumper stickers read. Yet, it’s important to remember that big agriculture corporations like Monsanto consistently fund and launch highly sophisticated research and development projects aimed toward improving production rates and promoting sustainability.

TechRepublic reported that Monsanto uses data analytics tools to help farmers achieve greater crop yields, employ fewer chemicals, and reduce water usage, leading to wider profit margins and more sustainable farming practices. The news source noted that the company estimated increased use of algorithmic information scrutiny could potentially lead to a $20 billion per year increase in worldwide crop production.

Starting at the ground level 
According to a study conducted in 2012 by PrecisionAg Institute, soybean growers that used data analysis applications reported average savings of 15 percent on expenses such as seed, fertilizer, fungicide, herbicide, and other chemicals. These deductions result in more affordable food products, enabling consumers of limited means to buy more.

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An “Expert Interview” with Kole Hicks, Sr. Director of Products at GoGrid

Thursday, May 8th, 2014 by

If you’re interested in learning the answers to many common Big Data implementation questions  Syncsort, a leading provider of Big Data integration solutions, recently posted an interesting blog with our very own Kole Hicks, Sr. Director of Products. In this interview, blogger Mark Underwood proposes several key questions to consider when beginning a Big Data project, starting with “What are the biggest obstacles?” and going all the way to “What are the in-house requirements for Big Data?”

Syncsort-blog

Check out the complete interview by clicking here.

And of course if you’re interested in a Big Data solutions integrator, the combination of Syncsort and GoGrid infrastructure might just be an ideal way to get you up and running with the push of a button!

You can learn more about Syncsort on its website.

HBase Made Simple

Wednesday, April 30th, 2014 by

GoGrid has just released its 1-Button Deploy™ of HBase, available to all customers in the US-West-1 data center. This technology makes it easy to deploy either a development or production HBase cluster on GoGrid’s high-performance infrastructure. GoGrid’s 1-Button Deploy™ technology combines the capabilities of one of the leading NoSQL databases with our expertise in building high-performance Cloud Servers.

HBase is a scalable, high-performance, open-source database. HBase is often called the Hadoop distributed database – it leverages the Hadoop framework but adds several capabilities such as real-time queries and the ability to organize data into a table-like structure. GoGrid’s 1-Button Deploy™ of HBase takes advantage of our SSD and Raw Disk Cloud Servers while making it easy to deploy a fully configured cluster. GoGrid deploys the latest Hortonworks’ distribution of HBase on Hadoop 2.0. If you’ve ever tried to deploy HBase or Hadoop yourself, you know it can be challenging. GoGrid’s 1-button Deploy™ does all the heavy lifting and applies all the recommended configurations to ensure a smooth path to deployment.

Why GoGrid Cloud Servers?

SSD Cloud Servers have several high-performance characteristics. They all come with attached SSD storage and large available RAM for the high I/O uses common to HBase. The Name Nodes benefit from the large RAM options available on SSD Cloud Servers and the Data Nodes use our Raw Disk Cloud Servers, which are configured as JBOD (Just a Bunch of Disks). This is the recommended disk configuration for Data Nodes, and GoGrid is one of the first providers to offer this configuration in a Cloud Server. Both SSD and Raw Disk Cloud Servers use a redundant 10-Gbps public and private network to ensure you have the maximum bandwidth to transfer your data. Plus, the cloud makes it easy to add more Data Nodes to your cluster as needed. You can use GoGrid’s 1-Button Deploy™ to provision either a 5-server development cluster or an 11-server production cluster with Firewall Service enabled.

Development Environments

The smallest recommended size for a development cluster is 5 servers. Although it’s possible to run HBase on a single server, you won’t be able to test failover or how data is replicated across nodes. You’ll most likely have a small database so you won’t need as much RAM, but will still benefit from SSD storage and a fast network. The Data Nodes use Raw Disk Cloud Servers and are configured with a replication factor of 3.

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