Archive for the ‘Big Data’ Category

 

How to Deploy a Riak Cluster in 5 Minutes on GoGrid

Friday, January 31st, 2014 by

The first big challenge to overcome with any new NoSQL database deployment is figuring out how to deploy the cluster in an environment that lets you scale as needed within a single data center and even across multiple data centers. To save cash, many customers make the mistake of trialing the product on cheap hardware with limited RAM across clusters that are inadequate for the application.

We think there’s a better way to run your evaluation. At GoGrid, we’ve made it possible to deploy a 5-node Riak cluster on beefy, high-performance machines with the click of a button. Check out the specs we’re providing as an orchestrated deployment using our 1-Button Deploy™ technology:

  • 5 nodes
  • 16 GB RAM per node
  • 16 cores per node
  • 640 GB storage per node
  • 10-Gbps network
  • 40-Gbps private network connectivity to additional Block Storage volumes (as needed)

Once the first cluster is deployed, you can point-and-click to add more nodes as you need them. Geek out for a moment on what you can do with this technology: You can run a user/session store for your application, use it to target and serve advertising, perform MapReduce operations, or any number of other things with just a few clicks of the mouse. And you can do it all in 4 easy steps.

Step 1: Login to GoGrid

To get started, login to your GoGrid account at https://my.gogrid.com to access the management console. If you don’t yet have an account, go ahead and create one: visit www.gogrid.com and click the Get Started button in the upper right-hand corner of the screen.

Step 2: Add New Infrastructure

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5 Ways Big Data Can Improve Your Business in 2014

Wednesday, January 29th, 2014 by

The concept of Big Data escapes many, but those who take the time to understand and use it benefit significantly. It’s predicted that Big Data will become huge in 2014, but companies that make business intelligence solutions a priority early in January will undoubtedly gain an edge over their competition. From data-based decisions to improving customer satisfaction, here are 5 ways Big Data can improve your business in 2014.

How big data will change your business in 2014

How Big Data will change your business in 2014

1. Access trends through social media
Big Data and the analytics that decipher it can easily zero in on a particular trend in social media and capitalize on it in ways that are advantageous to your business. Quobole uses the example of a location-based trend. That means a business can detect when a store is getting a notable amount of buzz on social media through unstructured data. Using that information, the business’s marketing team can then send out a blast on social media encouraging users to visit the store and give them an incentive to make a purchase. That’s just one example of capitalizing on social media trends with Big Data.

2. Rely less on gut feelings
In the past, many business decisions have been made based solely on instinct. Increasingly, however, companies are relying on data collected through market research or online trends. Big Data gives companies even more information to go on, enabling them to make business decisions that are more factually based and considerably less risky. If you have actual data, you can weigh real-life pros and cons before plunging into the deep end.

3. Stay ahead of the competition
Because Big Data is expected to go mainstream in the next year, businesses can get a head start on the competition by familiarizing themselves with business intelligence solutions. The sooner businesses start to use trends found by unstructured data, the larger head start they’ll have on the competition. It’s strategies like this that can turn underdog companies into market leaders and keep these top businesses at the forefront of their industry.

4. Make customers more satisfied
One of the biggest ways Big Data is changing businesses is by improving customer service. According to The Wall Street Journal, Netflix began using unstructured data for this purpose in 2008. After an outage, the company used the data to spot problem areas and improve the technology. It also used the data to inform the future viewing suggestions they offer customers. Big Data lets Netflix know where the most traffic is on their website and helps on-site engineers plan for better network capacity. Now, Netflix is a top company for on-demand Internet streaming.

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Big Data Cloud Servers for Hadoop

Monday, January 13th, 2014 by

GoGrid just launched Raw Disk Cloud Servers, the perfect choice for your Hadoop data node. These purpose-built Cloud Servers run on a redundant 10-Gbps network fabric on the latest Intel Ivy Bridge processors. What sets these servers apart, however, is the massive amount of raw storage in JBOD (Just  a Bunch of Disks) configuration. You can deploy up to 45 x 4 TB SAS disks on 1 Cloud Server.

These servers are designed to serve as Hadoop data nodes, which are typically deployed in a JBOD configuration. This setup maximizes available storage space on the server and also aids in performance. There are roughly 2 cores allocated per spindle, giving these servers additional MapReduce processing power. In addition, these disks aren’t a virtual allocation from a larger device. Each volume is actually a dedicated, physical 4 TB hard drive, so you get the full drive per volume with no initial write penalty.

Hadoop in the cloud

Most Hadoop distributions call for a name node supporting several data nodes. GoGrid offers a variety of SSD Cloud Servers that would be perfect for the Hadoop name node. Because they are also on the same 10-Gbps high-performance fabric as the Raw Disk Cloud Servers, SSD servers provide low latency private connectivity to your data nodes. I recommend using at least the X-Large SSD Cloud Server (16 GB RAM), although you may need a larger server, depending on the size of your Hadoop cluster. Because Hadoop stores metadata in memory, you’ll want more RAM if you have a lot of files to process. You can use any size Raw Disk Cloud Server, but you’ll want to deploy at least 3. Also, each Raw Disk Cloud Server has a different allocation of raw disks, which are illustrated in the table below. The Cloud Server in the illustration is the smallest size that has multiple disks per Cloud Server. Hadoop defaults to a replication factor of three, so to protect your data from failure, you’ll want to have at least 3 data nodes to distribute data. Although Hadoop attempts to replica data to different racks, there’s no guarantee that your Cloud Servers will be on different racks.

Note that the example below is for illustrative purposes only and is not representative of a typical Hadoop cluster; for example, most Cloudera and Hortonworks sizing guides start at 8 nodes. These configurations can differ greatly depending on if you intend to use the cluster for development, production, or production with HBase added. This includes the RAM and disk sizes (less of both for development, most likely more for HBase). Plus, if you’re thinking of using these nodes for production, you should consider adding a second name node.

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Big Data Holds Promise for Those Who Are Proactive

Friday, January 3rd, 2014 by

Companies are increasingly drawn to the Big Data market because of the potential benefits associated with embracing innovative information aggregation, management, storage, and analytics projects. This prospect has led organizations around the world to collect constantly expanding volumes of digital resources that promise to create opportunities to gain a competitive advantage, reduce costs, or improve overall operations.

Big data holds promise to those who are proactive

Big Data holds promise for those who are proactive

At the same time, however, many businesses have encountered unforeseen challenges in their race to collect large and complex data sets. Many of these issues derive from the fact that firms are simply gathering more information than they know how to handle, which is putting pressure on outdated infrastructure services and creating headache for the IT department and executives. Many enterprises overlook the fact that Big Data requires a specific management and organizational strategy, rather than simply an ad-hoc approach.

Understand what challenges lie ahead
Businesses that want to make the most out of Big Data must recognize which challenges will impact their bottom line and identify the steps that will allow their teams to overcome those obstacles. The truth is that databases are growing much faster than ever, which has led to new bottlenecks and other performance issues, including reduced processing speeds and the presence of unexpected costs associated with mitigating those problems.

Fortunately, building a Big Data strategy from the ground up can introduce new competitive opportunities without introducing unnecessary expenses or technical complications. Although a number of steps are required to construct these programs, business decision-makers should consider developing a new plan of action rather than relying on outdated philosophies that categorize storage as a dark and desolate vault that is virtually inaccessible.

Businesses now have an abundance of technologies at their fingertips, including cloud computing and sophisticated analytics, that allow employees to optimize operations without driving costs through the roof. The cloud in particular can be highly advantageous for Big Data storage purposes because of its scalable, on-demand nature. This approach lets companies continue their current trajectory and gather increasingly large volume of information without worrying about combating as many performance bottlenecks.

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Quality Customer Service Requires Predictive, Real-Time Insight

Friday, December 13th, 2013 by

In today’s increasingly crowded and fast-paced business world, organizations are continually pressured to meet the rapidly evolving needs of prospective and existing customers. In many cases, embracing Big Data strategies will enable firms to collect large amounts of information, analyze those assets, and turn them into meaningful insights that will help build better relationships with customers, ensure retention, and enhance loyalty.

Quality customer service requires predictive, real-time insight

Quality customer service requires predictive, real-time insight

Businesses that don’t provide customers with a unique, holistic experience will feel the repercussions of not doing so faster than ever. The proliferation of social media and other highly collaborative web services gives consumers a booming voice over the Internet. This phenomenon is encouraging organizations to adopt more innovative customer service programs that allow decision-makers to understand what customers want before those demands are requested. The only way to achieve these capabilities is through the use of Big Data analytics.

The real-time necessity
Executives, Support staff, and other customer service representatives need the ability to identify poor experiences as quickly as possible because waiting too long and allowing customers to leave an interaction on a bad note can results in major long-term consequences. Although monitoring conversations between corporate employees and consumers can help decision-makers gain more insight into how their agents are handling queries, this practice doesn’t necessarily provide the time needed to make adjustments before it’s too late.

Rather than taking this traditional reactive approach, companies should become proactive. Launching predictive analytic initiatives to collect large volumes of data on prospective and existing customers can help identify current and future trends. This information gives decision-makers a unique perspective on what customers actually want, making it easier to meet (and hopefully exceed) expectations while developing a reputation for responsiveness, innovation, and ease of use.

Overcoming unforeseen hurdles
Big Data technologies hold the promise of gaining a deeper understanding of customer behavior. However, decision-makers must guard against being too ambitious by collecting every scrap of information available in the hope that a certain piece will provide insight into a certain process. Embracing Big Data in this way can potentially lead to performance and efficiency problems, even if a company uses cloud computing technologies to match the scalable needs of increasingly large and complex information sets.

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