Archive for April, 2014


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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How can businesses make the most of their data?

Thursday, April 24th, 2014 by

When businesses attempt to harness Big Data, they’re looking to obtain actionable intelligence that can influence key business decisions. A variety of tools to do so are now available, but executives often get lost in the process of selecting which program would best suit their requirements. If a company needs to determine how a specific action will affect a particular industry, predictive analytics is probably the right choice for them. If a merchandiser wants to figure out how a single customer interacts with its brand, then descriptive tools may be the best option.

Organizing a plan to satisfy a customer.

Organizing a plan to satisfy a customer.

Know what you’re working with
Trying to draw conclusions from raw data aggregated onto cloud servers is both inefficient and ineffective. A company could collect all the data it wants, but if there’s no way of managing and segregating the information, then hastily made conclusions could send the company in the wrong direction. In addition, how professionals perceive the intelligence should not be manipulated by how they want to interpret it.

When it comes to understanding data, an open mind is mandatory. If tailored data displays a slight or entirely different angle on a particular situation, it’s better for management to adjust their plans according to the information as opposed to distorting the meaning of the digital information so that it better coincides with an original business strategy.

Interpreting phenomenon
Ultimately, data analytics gives C-suite professionals the ability to navigate through previously undecipherable patterns. ITWeb contributor Goran Dragosavac stated that there are three primary kinds of intelligence scrutiny platforms that draw considerably different conclusions from a single marketplace. Depending on what kind of business a particular company is in, the usefulness of each platform may vary significantly.

1. Predictive analytics examines the events of the past and present to determine which events will most likely transpire in the future. How can the current actions of a company manipulate the outcome? What should the business do to change the end result?

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When is “good enough” the right product decision?

Wednesday, April 23rd, 2014 by

“If you are not embarrassed by the first version of your product, you’ve launched too late.”
– Reid Hoffman, Founder, LinkedIn

Here’s the scenario: You started with a great idea, partnered with an excellent tech founder, and got $1M in funding so you could get the first release out the door. Part of your new-found funding went to hiring 3 Engineers. As the weeks of product development pass, you review the usability, demo it for prospects, and get feedback on how to make it better. The Engineers are working long hours to complete the first useful release for beta. When you review the usability with the Advisory Board or prospects, you get lots and lots of feedback about what works and what doesn’t, and you’re making changes—often daily.

One night you wake up and wonder, “Will we be tweaking this product forever? Will it ever get out the door so we can close some sales?” It’s time to have the conversation about what is “good enough” to ship. That means it’s time to revisit the original set of product requirements—the ones you and your team agreed needed to be implemented to ship the product. Go back to work with the team to completely scrub the bare minimum of what needs to be in the first version. Everyone will have opinions about what needs to be in the product when you ship. Justifications for including requirements may sound something like these:

“We won’t be able to reach one of our vertical market targets.”
“We’ll have a product that will only scale to 1M requests/timeframe and we need 10M.”
“Beta users hate the UI.”

During the scrub remember to ask, “What’s the cost of not implementing this functionality? Will we be able to add this functionality later without re-architecting the product?” Asking these questions lets you and the team make an informed business decision about minimal viable functionality. And at the end of your discussion, remember to reassure the team this sort of dialogue is healthy because it helps the company stay focused by prioritizing functionality into the releases on your road map—and ultimately drives your success.

A few years ago I had a great team that was working endless hours on a new workflow product. We started with requirements that were loosely defined and easily interpreted differently by each member of the team. Our usability expert seemed to re-interpret the same requirement each week, for example, but with the honest intent off making the product better. When it became clear we weren’t going to meet our functional complete date, I called the Engineers, PM, and QA together. As we scrubbed the requirements, we realized we were going to deliver 60% of what we originally thought was needed, but we still had very useful product. We finalized our definition by doing an in-scope/out-of-scope as a team for the rest of the company. And although it was a difficult conversation for the team to have, we delivered the first version—and got first mover advantage. So in the end, our 60%-ready first release actually turned out to be “good enough.”

How Public Organizations Should Treat Big Data

Tuesday, April 22nd, 2014 by

Though the “only human” argument certainly doesn’t apply to Big Data, enterprises and public organizations often expect too much out of the technology. Some executives are frustrated by results that don’t necessarily correlate with their predetermined business plans, and others consider one-time predictive conclusions to be final. The problem is, there’s no guarantee that analytical results will be “right.”

A government-themed action key

A government-themed action key

Public authorities interested in integrating Big Data into their cloud servers need to understand two things. First, digital information possess no political agenda, lacks emotion, and perceives the world in a completely pragmatic manner. And second, data changes as time progresses. For example, just because a county in Maine experienced a particularly rainy Spring doesn’t mean that farming soil will remain moist — future weather conditions may drastically manipulate the environment.

Benefiting from “incorrect” data
If a data analysis program harvests information from one source over the course of 1 hour and then attempts to develop conclusions, the system’s deductions will be correct to the extent that it accurately translated ones and zeroes into actionable intelligence. However, because the place from which the data was aggregated continues to produce new, variable knowledge, it may eventually contradict the original deduction.

Tim Hartford, a contributor to Financial Times, cited Google’s use of predictive analytics tools to chart how many people would be affected by influenza by using algorithms to scrutinize over 50 million search terms. The problem was, 4 years after the project was underway, the company’s system was disenfranchised by the Center for Disease Control and Prevention’s recent aggregation of data, showing that Google’s estimates of the spread of flu-like illnesses were overstated by a 2:1 ratio.

Taking the good with the bad
Although Hartford exemplified Google’s failure as a way of implying that Big Data isn’t what software developers are claiming it to be, Forbes contributor Adam Ozimek noted that the study displayed one of the advantages of the technology: The ability to reject conclusions due to consistently updated information. Furthermore, it’s important to note that Google only collected intelligence from one source, whereas the CDC was amassing data from numerous resources.

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What Cloud Computing Means for Industrial Infrastructure

Wednesday, April 16th, 2014 by

Just as cloud computing has revolutionized how corporate IT departments interact with their networks, the way in which business is conducted across all markets has also changed significantly. Because the technology provides employees with a different way of performing tasks, the manner in which managers and executives make decisions has been radically influenced by an influx of data points.

A construction grew surveys an ongoing project.

A construction crew surveys an ongoing project.

When it comes to traditional business practices, everything has become a lot easier thanks to cloud computing. For most large enterprises, it’s not an arduous chore for employees to access a Word document from a tablet, edit the file, and share it with coworkers. As far as the industrial sector is concerned, reporting mechanical deficiencies or malfunctions can happen in near real time because many workers are now equipped with smartphones, some of them supplied by their employers.

Digital information changes everything 
In an interview with InformationWeek, former Chief Cloud Architect for Netflix Adrian Cockcroft noted that a strong integration of all teams and departments is imperative for a company to ensure its survival. Cockcroft spent 7 years with the company developing the necessary architecture to launch new ways of finding and showcasing films. In 2008, Netflix ceased operating through on-premise databases and moved to cloud servers. Afterward, the former CCA began noticing some fundamental changes throughout the organization.

Cockcroft told the news source that the increased speed and flexibility offered by the off-premise solution gave Netflix its competitive edge. During its fledgling years, the company’s size couldn’t compare to that of its competitors, requiring it to develop and act on particular incentives quicker than others film distributors. Basically, the company had to make a consorted effort to eliminate inefficient communication between software designers and engineers.

“We put a high-trust, low-process environment in place with few hand-offs between teams,” said Cockcroft.

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