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Under The Radar 2012 Recap & Analysis – Summing Up Some Secret Startup Sauce (Part 2)

Thursday, May 3rd, 2012 by

I recently attended Under the Radar 2012 as GoGrid was a sponsor of this event. As there were several tracks, Michael Sheehan and I split the tracks and I covered Infrastructure, Database Scalability and Big Data. Michael covered Mobile Access, Infrastructure, Performance Monitoring, PaaS in Part 1.  Overall, the presenting companies have some compelling ideas and it gives an indicator as to the new thinking happening in Silicon Valley. The trends that I noticed were: a continued interest in private clouds, the increase in adoption of Openstack and the prevalence integrating Big Data.

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If you never attended Under the Radar, the format is to have four startups that already have a real product present for 6 minutes and are then judged by a panel of experienced executives at more established companies. The presenters had to be companies that are actual startups with a unique value proposition and a real product that they are able to monetize. Alumni or companies that are already more established can also present as a “Grad Circle” member but they are not included in the awards presented at the end of the show. And like American Idol, the audience also has a vote on their favorites for each category.  I included the Judge’s choice and Audience choice for each category but also added my own choice which reflects my own opinion and not that of GoGrid.

Infrastructure

This category focused on companies that are delivering infrastructure or infrastructure management products. So this would include services that could offer up infrastructure components (like compute, network, and storage) or even tools for managing configurations and deployments. Not surprisingly, nearly all of them focus on the cloud as the operating model of choice.

Cloudscaling – This company focuses on delivering an amazon-like cloud using Openstack. Their solution is comprised of Open Cloud OS, which is a product grade version of Openstack, Cloudblocks, a comprehensive architecture for cloud services and Hardware Blueprints, which are templates for physical hardware. Customers can leverage this solution to deploy a public or private cloud in their own DC.

(more…) «Under The Radar 2012 Recap & Analysis – Summing Up Some Secret Startup Sauce (Part 2)»

The Big Data Revolution – Part 2 – Enter the Cloud

Wednesday, March 21st, 2012 by

In Part 1 of this Big Data series, I provided a background on the origins of Big Data.

But What is Big Data?

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The problem with using the term “Big Data” is that it’s used in a lot of different ways. One definition is that Big Data is any data set that is too large for on-hand data management tools. According to Martin Wattenberg, a scientist at IBM, “The real yardstick … is how it [Big Data] compares with a natural human limit, like the sum total of all the words that you’ll hear in your lifetime.” Collecting that data is a solvable problem, but making sense of it, (particularly in real time), is the challenge that technology tries to solve. This new type of technology is often listed under the title of “NoSQL” and includes distributed databases that are a departure from relational databases like Oracle and MySQL. These are systems that are specifically designed to be able to parallelize compute, distribute data, and create fault tolerance on a large cluster of servers. Some examples of NoSQL projects and software are: Hadoop, Cassandra, MongoDB, Riak and Membase.

The techniques vary, but there is a definite distinction between SQL relational databases and their NoSQL brethren. Most notably, NoSQL systems share the following characteristics:

  • Do not use SQL as their primary query language
  • May not require fixed table schemas
  • May not give full ACID guarantees (Atomicity, Consistency, Isolation, Durability)
  • Scale horizontally

Because of the lack of ACID, NoSQL is used when performance and real-time results are more important than consistency. For example, if a company wants to update their website in real time based on an analysis of the behaviors of a particular user interaction with the site, they will most likely turn to NoSQL to solve this use case.

However, this does not mean that relational databases are going away. In fact, it is likely that in larger implementations, NoSQL and SQL will function together. Just as NoSQL was designed to solve a particular use case, so do relational databases solve theirs. Relational databases excel at organizing structured data and is the standard for serving up ad-hoc analytics and business intelligence reporting. In fact, Apache Hadoop even has a separate project called Sqoop that is designed to link Hadoop with structured data stores. Most likely, those who implement NoSQL will maintain their relational databases for legacy systems and for reporting off of their NosQL clusters.

(more…) «The Big Data Revolution – Part 2 – Enter the Cloud»

The Big Data Revolution – Part 1 – The Origins

Tuesday, March 20th, 2012 by

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For many years, companies collected data from various sources that often found its way to relational databases like Oracle and MySQL. However, the rise of the internet and Web 2.0, and recently social media began not only an enormous increase in the amount of data created, but also in the type of data. No longer was data relegated to types that easily fit into standard data fields – it now came in the form of photos, geographic information, chats, Twitter feeds and emails. The age of Big Data is upon us.

A study by IDC titled “The Digital Universe Decade” projects a 45-fold increase in annual data by 2020. In 2010, the amount of digital information was 1.2 zettabytes. 1 zettabyte equals 1 trillion gigabytes. To put that in perspective, the equivalent of 1.2 zettabytes is a full-length episode of “24” running continuously for 125 million years, according to IDC. That’s a lot of data. More importantly, this data has to go somewhere, and this report projects that by 2020, more than 1/3 of all digital information created annually will either live in or pass through the cloud. With all this data being created, the challenge will be to collect, store, and analyze what it all means.

Business intelligence (BI) systems have always had to deal with large data sets. Typically the strategy was to pull in “atomic” -level data at the lowest level of granularity, then aggregate the information to a consumable format for end users. In fact, it was preferable to have a lot of data since you could also “drill-down” from the aggregation layer to get at the more detailed information, as needed.

Large Data Sets and Sampling

Coming from a data background, I find that dealing with large data sets is both a blessing and a curse. One product that I managed analyzed share of wireless numbers. The number of wireless subscribers in 2011 according to CTIA was 322.9 million and growing. While that doesn’t seem like a lot of data at first, if each wireless number was a unique identifier, there could be any number of activities associated with each number. Therefore the amount of information generated from each number could be extensive, especially as the key element was seeing changes over time. For example, after 2003, mobile subscribers in the United States were able to port their numbers from one carrier to another. This is of great importance to market research since a shift from one carrier to another would indicate churn and also impact the market share of carriers in that Metropolitan Statistical Area (MSA).

Given that it would take a significant amount of resources to poll every household in the United States, market researchers often employ a technique called sampling. This is a statistical technique where a panel that represents the population is used to represent the activity of the overall population that you want to measure. This is a sound scientific technique if done correctly but its not without its perils. For example, it’s often possible to get +/- 1% error at 95% confidence for a large population but what happens once you start drilling down into more specific demographics and geographies? The risk is not only having enough sample (you can’t just have one subscriber represent the activity of a large group for example) but also ensuring that it is representative (is the subscriber that you are measuring representative of the population that you want to measure?). It’s a classic problem of using panelists that sampling errors do occur. It’s fairly difficult to be completely certain that your sample is representative unless you’ve actually measured the entire population already (using it as a baseline) but if you’ve already done that, why bother sampling?

(more…) «The Big Data Revolution – Part 1 – The Origins»

Spotify Music Apps Hack Weekend – Sponsored by GoGrid

Tuesday, February 28th, 2012 by

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To celebrate the release of their API, Spotify sponsored a Hack-a-thon at SPiN Ping Pong Club in New York City from Friday February 24 until Sunday February 26. Spotify was joined by big brands like Doritos, CW, McDonald’s, Showtime, State Farm and Mountain Dew. Technology companies sponsoring the event included Facebook, Twilio, FourSquare, The Echo Nest and of course, GoGrid. GoGrid provided all the cloud servers for the event to support the developers as they created brand new apps using the Spotify API in conjunction with other API like Facebook’s Open Graph. GoGrid’s manager of cloud ecosystem, Paul Lancaster and I were on-hand to meet with developers and provide support for the event.

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50 CentOS x64 cloud servers were provisioned to the hackers by GoGrid to build their applications free of charge with root level access for maximum flexibility. Hundreds of hackers showed up to build the next great apps and were treated to live performances by Blood Orange and MNDR. While hack-a-thons tend to have attrition over time, hackers stayed throughout the night and most for the entire weekend.

Museik App

There were roughly 30 projects worked on during the weekend which ranged from an app called Museik (UI shown above) that extracts content from the internet related to the release date of a song on Spotify to a project called Orbidal by the students of the VCU Brandcenter that gathers the collective feelings of your Facebook feed and creates a playlist based on that mood on Spotify.

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(more…) «Spotify Music Apps Hack Weekend – Sponsored by GoGrid»

Riverbed Stingray 8.1 Now in the GoGrid Cloud!

Tuesday, February 7th, 2012 by

As of today, GoGrid has released multiple images of the leading software load balancer, Riverbed Stingray! The following images are available on the GoGrid Partner Exchange in both San Francisco and Amsterdam:

  • Riverbed 7.4 Simple Load Balancer 10 Mbps
  • Riverbed 8.1 Load Balancer 10 Mbps
  • Riverbed 8.1 Load Balancer 200 Mbps
  • Riverbed 8.1 Load Balancer 200 Mbps WAF