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Address Your Company’s Data Explosion with Storage That Scales
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This is a duplicate of a blog I authored for SUSE, originally published at the SUSE Blog Site.
Experts predict that our world will generate 44 ZETTABYTES of digital data by 2020.
How about some context?
Now, you may think that these are all teenage selfies and funny cat videos – in actuality, much of it is legitimate data your company will need to stay competitive and to serve your customers.
The Data Explosion Happening in YOUR Industry
Some interesting factoids:
- An automated manufacturing facility can generate many terabytes of data in a single hour.
- In the airline industry, a commercial airplane can generate upwards of 40 TB of data per hour.
- Mining and drilling companies can gather multiple terabytes of data per minute in their day-to-day operations.
- In the retail world, a single store can collect many TB of customer data, financial data, and inventory data.
- Hospitals quickly generate terabytes of data on patient health, medical equipment data, and patient x-rays.
The list goes on and on. Service providers, telecommunications, digital media, law enforcement, energy companies, HPC research groups, governments, the financial world, and many other industries (including yours) are experiencing this data deluge now.
And with terabytes of data being generated by single products by the hour or by the minute, the next stop is coming up quick: PETABYTES OF DATA.
Status Quo Doesn’t Cut It
I know what you’re thinking: “What’s the problem? I‘ve had a storage solution in place for years. It should be fine.”
Not quite.
- You are going to need to deal with a LOT more data than you are storing today in order to maintain your competitive edge.
- The storage solutions you’ve been using for years have likely not been designed to handle this unfathomable amount of data.
- The costs of merely “adding more” of your current storage solutions to deal with this amount of data can be extremely expensive.
The good news is that there is a way to store data at this scale with better performance at a much better price point.
Open Source Scale Out Storage
Why is this route better?
- It was designed from the ground up for scale.
Much like how mobile devices changed the way we communicate / interact / take pictures / trade stock, scale out storage is different design for storage. Instead of all-in-one storage boxes, it uses a “distributed model” – farming out the storage to as many servers / hard drives as it has access to, making it very scalable and very performant. (Cloud environments leverage a very similar model for computing.) - It’s cost is primarily commodity servers with hard drives and software.
Traditional storage solutions are expensive to scale in capacity or performance. Instead of expensive engineered black boxes, we are looking at commodity servers and a bit of software that sits on each server – you then just add a “software + server” combo as you need to scale. - When you go open source, the software benefits get even better.
Much like other open source technologies, like Linux operating systems, open source scale out storage allows users to take advantage of rapid innovation from the developer communities, as well as cost benefits which are primarily support or services, as opposed to software license fees.
Ready. Set. Go.
At SUSE, we’ve put this together in an offering called SUSE Enterprise Storage, an intelligent software-defined storage management solution, powered by the open source Ceph project.
It delivers what we’ve talked about: open source scale out storage. It scales, it performs, and it’s open source – a great solution to manage all that data that’s coming your way, that will scale as your data needs grow.
And with SUSE behind you, you’ll get full services and support to any level you need.
OK, enough talk – it’s time for you to get started.
And here’s a great way to kick this off: Go get your FREE TRIAL of SUSE Enterprise Storage. Just click this link, and you’ll be directed to the site (note you’ll be prompted to do a quick registration.) It will give you quick access to the scale out storage tech we’ve talked about, and you can begin your transition over to the new evolution of storage technology.
Until next time,
JOSEPH
@jbgeorge
Tech in Real Life: Content Delivery Networks, Big Data Servers and Object Storage
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This is a duplicate of a blog I authored for HP, originally published at hp.nu/Lg3KF.
In a joint blog authored with theCube’s John Furrier and Scality’s Leo Leung, we pointed out some of the unique characteristics of data that make it act and look like a vector.
At that time, I promised we’d delve into specific customer uses for data and emerging data technologies – so let’s begin with our friends in the telecommunications and media industries, specifically around the topic of content distribution.
But let’s start at a familiar point for many of us…
If you’re like most people, when it comes to TV, movies, and video content, you’re an avid (sometimes binge-watching) fan of video streaming and video on-demand. More and more people are opting to view content via streaming technologies. In fact, a growing number of broadcast shows are viewed on mobile and streaming devices, as are a number of live events, such as this year’s NCAA basketball tournament via streaming devices.
These are fascinating data points to ponder, but think about what goes on behind them.
How does all this video content get stored, managed, and streamed?
Suffice it to say, telecom and media companies around the world are addressing this exact challenge with content delivery networks (CDN). There are a variety of interesting technologies out there to help develop CDNs, and one interesting new technology to enable this is object storage, especially when it comes to petabytes of data.
Here’s how object storage helps when it comes to streaming content.
- With streaming content comes a LOT of data. Managing and moving that data is a key area to address, and object storage handles it well. It allows telecom and media companies to effectively manage many petabytes of content with ease – many IT options lack that ability to scale. Features in object storage like replication and erasure coding allow users to break large volumes of data into bite size chunks, and disperse it over several different server nodes, and often times, several different geographic locations. As data is needed, it is rapidly re-compiled and distributed as needed.
- Raise your hand if you absolutely love to wait for your video content to load. (Silence.) The fact is, no one likes to see the status bar slowly creeping along, while you’re waiting for zombies, your futbol club, or the next big singing sensation to show up on the screen. Because object storage technologies are able to support super high bandwidth and millions of HTTP requests per minute, any customer looking to distribute media is able to allow their customers access to content with superior performance metrics. It has a lot to do with the network, but also with the software managing the data behind the network, and object storage fits the bill.
These are just two of the considerations, and there are many others, but object storage becomes an interesting technology to consider if you’re looking to get content or media online, especially if you are in the telecom or media space.
Want a real life example? Check out how our customer RTL II, a European based television station, addressed their video streaming challenge with object storage. It’s all detaile here in this case study – “RTL II shifts video archive into hyperscale with HP and Scality.” Using HP ProLiant SL4540 big data servers and object storage software from HP partner Scality, RTL II was able to boost their video transfer speeds by 10x!
Webinar this week! If this is a space you could use more education on, Scality and HP will be hosting a couple of webinars this week, specifically around object storage and content delivery networks. If you’re looking for more on this, be sure to join us – here are the details:
Session 1 (Time-friendly for European and APJ audiences)
- Who: HP’s big data strategist, Sanjeet Singh, and Scality VP, Leo Leung
- Date: Wed, Apr 8, 2015
- Time: 3pm Central Europe Summer / 8am Central US
- Registration Link
Session 2 (Time-friendly for North American audiences)
- Who: HP Director, Joseph George, and Scality VP, Leo Leung
- Date: Wed, Apr 8, 2015
- Time: 10am Pacific US / 12 noon Central US
- Registration Link
And as always, for any questions at all, you can always send us an email at BigDataEcosystem@hp.com or visit us at www.hp.com/go/ProLiant/BigDataServer.
And now off to relax and watch some TV – via streaming video of course!
Until next time,
JOSEPH
@jbgeorge
Recognizing the Layers of Critical Insight That Data Offers
This is a joint blog I did with John Furrier of SiliconAngle / theCube and Leo Leung from Scality, originally published at http://bit.ly/1E6nQuR
Data is an interesting concept.
During a recent CrowdChat a number of us started talking about server based storage, big data, etc., and the topic quickly developed into a forum on data and its inherent qualities. The discussion led us to realize that data actually has a number of attributes that clearly define it – similar to how a vector has both a direction and magnitude.
Several of the attributes we uncovered as we delved into this notion of data as a vector include:
- Data Gravity: This was a concept developed by my friend, Dave McCrory, a few years ago, and it is a burgeoning area of study today. The idea is that as data is accumulated, additional services and applications are attracted to this data – similar to how a planet’s gravitational pull attracts objects to it. An example would be the number 10. If you the “years old” context is “attracted” to that original data point, it adds a certain meaning to it. If the “who” context is applied to a dog vs. a human being, it takes on additional meaning.
- Relative Location with Similar Data: You could argue that this is related to data gravity, but I see it as more of a poignant a point that bears calling out. At a Hadoop World conference many years ago, I heard Tim O’Reilly make the comment that our data is most meaningful when it’s around other data. A good example of this is medical data. Health information of a single individual (one person) may lead to some insights, but when placed together with data from a members of a family, co-workers on a job location, or the citizens of a town, you are able to draw meaningful conclusions. When grouped with other data, individual pieces of data take on more meaning.
- Time: This came up when someone posed the question “does anyone delete data anymore?” With the storage costs at scale becoming more and more affordable, we concluded that there is no longer an economic need to delete data (though there may be regulatory reasons to do so). Then came the question of determining what data was not valuable enough to keep, which led to the epiphany that data that might be viewed as not valuable today, may become significantly valuable tomorrow. Medical information is a good example here as well – capturing the data that certain individuals in the 1800’s were plagued with a specific medical condition may not seem meaningful at the time, until you’ve tracked data on specific descendants of his family being plagued by similar ills over the next few centuries. It is difficult to quantify the value of specific data at the time of its creation.
In discussing this with my colleagues, it became very clear how early we are in the evolution of data / big data / software defined storage. With so many angles yet to be discussed and discovered, the possibilities are endless.
This is why it is critical that you start your own journey to salvage the critical insights your data offers. It can help you drive efficiency in product development, it can help you better serve you constituents, and it can help you solve seemingly unsolvable problems. Technologies like object storage, cloud based storage, Hadoop, and more are allowing us to learn from our data in ways we couldn’t imagine 10 years ago.
And there’s a lot happening today – it’s not science fiction. In fact, we are seeing customers implement these technologies and make a turn for the better – figuring out how to treat more patients, enabling student researchers to share data across geographic boundaries, moving media companies to stream content across the web, and allowing financial institutions to detect fraud when it happens. Though the technologies may be considered “emerging,” the results are very, very real.
Over the next few months, we’ll discuss specific examples of how customers are making this work in their environments, tips on implementing these innovative technologies, some unique innovations that we’ve developed in both server hardware and open source software, and maybe even some best practices that we’ve developed after deploying so many of these big data solutions.
Stay tuned.
Until next time,
Joseph George – @jbgeorge
Director, HP Servers
Leo Leung – @lleung
VP, Scality
John Furrier – @furrier
Founder of SiliconANGLE Media
Cohost of @theCUBE
CEO of CrowdChat
The JBGeorge Tech Blog 