Archive
Address Your Company’s Data Explosion with Storage That Scales
.
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
.
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 HP Big Data Reference Architecture: It’s Worth Taking a Closer Look…
This is a duplicate of the blog I’ve authored on the HP blog site at http://h30507.www3.hp.com/t5/Hyperscale-Computing-Blog/The-HP-Big-Data-Reference-Architecture-It-s-Worth-Taking-a/ba-p/179502#.VMfTrrHnb4Z
I recently posted a blog on the value that purpose-built products and solutions bring to the table, specifically around the HP ProLiant SL4540 and how it really steps up your game when it comes to big data, object storage, and other server based storage instances.
Last month, at the Discover event in Barcelona, we announced the revolutionary HP Big Data Reference Architecture – a major step forward in how we, as a community of users, do Hadoop and big data – and it is a stellar example of how purpose-built solutions can revolutionize how you accelerate IT technology, like big data. We’re proud that HP is leading the way in driving this new model of innovation, with the support and partnership of the leading voices in Hadoop today.
Here’s the quick version on what the HP Big Data Reference Architecture is all about:
Think about all the Hadoop clusters you’ve implemented in your environment – they could be pilot or production clusters, hosted by developer or business teams, and hosting a variety of applications. If you’re following standard Hadoop guidance, each instance is most likely a set of general purpose server nodes with local storage.
For example, your IT group may be running a 10 node Hadoop pilot on servers with local drives, your marketing team may have a 25 node Hadoop production cluster monitoring social media on similar servers with local drives, and perhaps similar for the web team tracking logs, the support team tracking customer cases, and sales projecting pipeline – each with their own set of compute + local storage instances.
There’s nothing wrong with that set up – It’s the standard configuration that most people use. And it works well.
However….
Just imagine if we made a few tweaks to that architecture.
- What if we replaced the good-enough general purpose nodes, and replaced them with purpose-built nodes?
- For compute, what if we used HP Moonshot, which is purpose-built for maximum compute density and price performance?
- For storage, what if we used HP ProLiant SL4540, which is purpose-built for dense storage capacity, able to get over 3PB of capacity in a single rack?
- What if we took all the individual silos of storage, and aggregated them into a single volume using the purpose-built SL4540? This way all the individual compute nodes would be pinging a single volume of storage.
- And what if we ensured we were using some of the newer high speed Ethernet networking to interconnect the nodes?
Well, we did.
And the results are astounding.
While there is a very apparent cost benefit and easier management, there is a surprising bump in performance in terms of read and write.
It was a surprise to us in the labs, but we have validated it in a variety of test cases. It works, and it’s a big deal.
And Hadoop industry leaders agree.
“Apache Hadoop is evolving and it is important that the user and developer communities are included in how the IT infrastructure landscape is changing. As the leader in driving innovation of the Hadoop platform across the industry, Cloudera is working with and across the technology industry to enable organizations to derive business value from all of their data. We continue to extend our partnership with HP to provide our customers with an array of platform options for their enterprise data hub deployments. Customers today can choose to run Cloudera on several HP solutions, including the ultra-dense HP Moonshot, purpose-built HP ProLiant SL4540, and work-horse HP Proliant DL servers. Together, Cloudera and HP are collaborating on enabling customers to run Cloudera on the HP Big Data architecture, which will provide even more choice to organizations and allow them the flexibility to deploy an enterprise data hub on both traditional and newer infrastructure solutions.” – Tim Stevens, VP Business and Corporate Development, Cloudera
“We are pleased to work closely with HP to enable our joint customers’ journey towards their data lake with the HP Big Data Architecture. Through joint engineering with HP and our work within the Apache Hadoop community, HP customers will be able to take advantage of the latest innovations from the Hadoop community and the additional infrastructure flexibility and optimization of the HP Big Data Architecture.” – Mitch Ferguson, VP Corporate Business Development, Hortonworks
And this is just a sample of what HP is doing to think about “what’s next” when it comes to your IT architecture, Hadoop, and broader big data. There’s more that we’re working on to make your IT run better, and to lead the communities to improved experience with data.
If you’re just now considering a Hadoop implementation or if you’re deep into your journey with Hadoop, you really need to check into this, so here’s what you can do:
- my pal, Greg Battas posted on the new architecture and goes technically deep into it, so give his blog a read to learn more about the details.
- Hortonworks has also weighed in with their own blog.
If you’d like to learn more, you can check out the new published reference architectures that follow this design featuring HP Moonshot and ProLiant SL4540:
- HP Big Data Reference Architecture: Cloudera Enterprise reference architecture implementation
- HP Big Data Reference Architecture: Hortonworks Data Platform reference architecture implementation
If you’re looking for even more information, reach out to your HP rep and mention the HP Big Data Reference Architecture. They can connect you with the right folks to have a deeper conversation on what’s new and innovative with HP, Hadoop, and big data. And, the fun is just getting started – stay tuned for more!
Until next time,
JOSEPH
Purpose-Built Solutions Make a Big Difference In Extracting Data Insights: HP ProLiant SL4500
This is a duplicate of the blog I’ve authored on the HP blog site at http://h30507.www3.hp.com/t5/Hyperscale-Computing-Blog/Purpose-Built-Solutions-Make-a-Big-Difference-In-Extracting-Data/ba-p/173222#.VEUdYrEo70c
Indulge me as I flash back to the summer of 2012 at the Aquatics Center in London, England – it’s the Summer Olympics, where some of the world’s top swimmers, representing a host of nations, are about to kick off the Men’s 100m Freestyle swimming competition. The starter gun fires, and the athletes give it their all in a heated head to head match for the gold.
And the results of the race are astounding: USA’s Nathan Adrian took the gold medal with a time of 47.52 seconds, with Australia’s James Magnussen finishing a mere 0.01 seconds later to claim the silver medal! It was an incredible display of competition, and a real testament to power of the human spirit.
For an event demanding such precise timing, we can only assume that very sensitive and highly calibrated measuring devices were used to capture accurate results. And it’s a good thing they did – fractions of a second separated first and second place.
Now, you and I have both measured time before – we’ve checked our watches to see how long it has been since the workday started, we’ve used our cell phones to see how long we’ve been on the phone, and so on. It got the job done. Surely the Olympic judges at the 2012 Men’s 100m Freestyle had some of these less precise options available – why didn’t they just simply huddle around one of their wrist watches to determine the winner of the gold, silver and bronze?
OK, I am clearly taking this analogy to a silly extent to make a point.
When you get serious about something, you have to step up your game and secure the tools you need to ensure the job gets done properly.
There is a real science behind using purpose-built tools to solve complex challenges, and the same is true with IT challenges, such as those addressed with big data / scale out storage. There are a variety of infrastructure options to deal with the staggering amounts of data, but there are very few purpose built server solutions like HP’s ProLiant SL4500 product – a server solution built SPECIFCIALLY for big data and scale out storage.
The HP ProLiant SL4500 was built to handle your data. Period.
- It provides an unprecedented drive capacity with over THREE PB in a single rack
- It delivers scalable performance across multiple drive technologies like SSD, SAS or SATA
- It provides significant energy savings with shared cooling and power and reduced complexity with fewer cables
- It offers flexible configurations •A 1-node, 60 large form factor drive configuration, perfect for large scale object storage with software vendors like Cleversafe and Scality, or with open source projects like OpenStack Swift and Ceph
- A 2-node, 25 drive per node configuration, ideal for running Microsoft Exchange
- A 3-node, 15 drive per node configuration, optimal for running Hadoop and analytics applications
If you’re serious about big data and scale out storage, it’s time to considering stepping up your game with the SL4500. Purpose-built makes a difference, and the SL4500 was purpose-built to help you make sense of your data.
You can learn more about the SL4500 by talking to your HP rep or by visiting us online at HP ProLiant SL4500 Scalable Systems or at Object Storage Software for ProLiant.
And if you’re here at Hadoop World this week, come on by the HP booth – we’d love to chat about how we can help solve your data challenges with SL4500 based solutions.
Until next time,
Joseph George
Day 2: Big Data Innovation Summit 2014 #DataWest14
.
Hello again big data fans – from where I’ve learned the San Francisco 49’ers will be playing their 2014 NFL season at Levi’s Stadium… Santa Clara!
(BTW, the stadium – from what I could see – is beautiful! I’m a big NFL fan, and there’s now another reason to come to the San Jose area, other than all the cloud / big data conferences.)
Got a lot of great feedback on yesterday’s “Day 1” post of the summit, so here are some observations from the final day of the conference.
- Yahoo’s Duru Ahanotu spoke through driving efficiency in how data teams are organized, going through the permutations of generalists vs specialists and centralized vs de-centralized, and how to best address teams in each model.
. - PayPal’s Moises Nascimento (who is a very captivating speaker) drove the point home, that though we are now adopting many of the new data technologies like Hadoop and NoSQL, most of our existing data sources and toolsets still provide value – so there is value in leveraging ALL data sources.
. - Moises also made a point of highlighting that data manipulation is best handled at the SYSTEM level, while data analysis is better managed at the ENTERPRISE level
. - In HP’s discussion, they introduced the concept of the GEOBYTE – 10^30 bytes, a size of data that the human race is expected to hit in the next few years.
To provide context on the magnitude of a GEOBYTE (10^30 bytes), there is estimated to only be 10^19 GRAINS OF SAND ON THE EARTH. Think about that for a second.
- The team also highlighted their view on “Big BI” vs “Big Data”
- Big BI – same types of analysis but on more data; more batch processing; results that were not easily actionable
- Big Data – joining datasets that have not been previously joined, near real time analysis, action oriented results
.
- I thought Ancestry.com had one of the best sessions of the event, as they went deep into the GERMLINE algorithm that was the foundation of their business technology, and how they had to create jermline (now with a “j”) based on Hadoop / HDFS to create a SCALABLE matching engine. As we all know, SCALE matters. The performance and speed benchmarks between the “G” project and the “j” project were mindblowing.
. - Finally, sat in on the Netflix session – in addition to being a big fan of Netflix, as both a consumer and a tech observer, I’ve always been impressed with the way Netflix has evolved their business, and continues to do so. In this session, they went into great detail on their use of the Amazon cloud services, and their open source projects as a layer above to enhance functionality and deploy features. Topics touched on included red / black deployment to allow ease of features into production, and the importance of graceful degradation, so that a failure can be less of a catastrophic event for the end user.
.- One very telling statement is really a commentary on the value of use and participation in the open source process – Netflix was clear that they see value in being an open source contributor / leader is that it preserves the future of their systems – rather than sitting back and letting the industry decide their direction with tools and tech, Netflix uses open source to help drive and lead the industry to where they see value.
.
- One very telling statement is really a commentary on the value of use and participation in the open source process – Netflix was clear that they see value in being an open source contributor / leader is that it preserves the future of their systems – rather than sitting back and letting the industry decide their direction with tools and tech, Netflix uses open source to help drive and lead the industry to where they see value.
- (I did resist the urge to ask the Netflix presenter when the next season of “House of Cards” would come out. 🙂 )
.
One of the frequent questions that came up at the Dell booth was “what is Dell doing in big data?”
The answer? Actually… quite a bit, and for quite a while.
Between the Dell Apache Hadoop HW+SW+Services Solution, the Toad BI suite, the Kitenga analytics toolsets, and our growing HPC business, Dell has been a part of this movement since its early days. I’d recommend you drop us a line at Hadoop@Dell.com or visit us at http://www.Dell.com/Hadoop to learn more.
If you were out at the show this week, be sure to leave a comment on your thoughts as well.
Hope everyone has safe trips home, and we’ll see you at the next big data get-together!
Until next time,
JBG
@jbgeorge
Day 1: Big Data Innovation Summit 2014
.
Hello from sunny, Santa Clara!
My team and I are here at the BIG DATA INNOVATION SUMMIT representing Dell (the company I work for), and it’s been a great day one.
I just wanted to take a few minutes to jot down some interesting ideas I heard today:
- In Daniel Austin’s keynote, he addressed that the “Internet of things” should really be the “individual network of things” – highlighting that the number of devices, their connectivity, their availability, and their partitioning is what will be key in the future.
. - One data point that also came out of Daniel’s talk – every person is predicted to generate 20 PETABYTES of data over the course of a lifetime!
. - Juan Lavista of Bing hit on a number of key myths around big data:
- the most important part of big data is its size
- to do big data, all you need is Hadoop
- with big data, theory is no longer needed
- data scientists are always right 🙂
QUOTE OF THE DAY: “Correlation does not yield causation.” – Juan Lavista (Bing)
- Anthony Scriffignano was quick to admonish the audience that “it’s not just about data, it’s not just about the math… [data] relationships matter.”
. - The state of Utah state government is taking a very progressive view to areas that analytics can help drive efficiency in at that level – census data use, welfare system fraud, etc. And it appears Utah is taking a leadership position in doing so.
I also had the privilege of moderating a panel on the topic of the convergence between HPC and the big data spaces, with representatives on the panel from Dell (Armando Acosta), Intel (Brent Gorda), and the Texas Advanced Computing Center (Niall Gaffney). Some great discussion about the connections between the two, plus tech talk on the Lustre plug-in and the SLURM resource management project.
Additionally, Dell product strategists Sanjeet Singh and Joey Jablonski presented on a number of real user implementations of big data and analytics technologies – from university student retention projects to building a true centralized, enterprise data hub. Extremely informative.
All in all, a great day one!
If you’re out here, stop by and visit us at the Dell booth. We’ll be showcasing our hadoop and big data solutions, as well as some of the analytics capabilities we offer.
(We’ll also be giving away a Dell tablet on Thursday at 1:30, so be sure to get entered into the drawing early.)
Stay tuned, and I’ll drop another update tomorrow.
Until next time,
JOSEPH
@jbgeorge
Michael Dell Comments on the “Data Economy”
This is a repost of my blog at http://dell.to/1euUcqk.
In this short interview with Inc., Michael Dell provides an overview of the company’s transformation into a leading player in the “data economy.”
As Michael notes, with the costs of collecting data decreasing, more companies in a growing number of industries are making better use of existing data sources, and gathering data from new sources.
And that’s where Dell has been enabling customers for years with solutions built with technologies like Hadoop and NoSql. Helping companies and organizations make better use of this data, and assisting them in using it to solve their challenges, are just a few of the ways Dell has changed the Big Data conversation, and built an entirely new enterprise business along the way.
As a member of the Technology CEO Council, Michael also recently joined other tech CEOs to discuss the data economy with policy makers. As an example of the potential of the data economy, he explained how Dell’s growing health information technology practice includes 7 billion medical images. These images are in an aggregated data set allowing researchers to mine them for patterns and predictive analytics.
“There’s lots that can be done with this data that was very, very siloed in the past,” Michael toldComputerworld, “We’re really just kind of scratching the surface.”
It’s certainly an exciting time to be at Dell – and the data revolution continues!
NOW HIRING: Cloud and Big Data Solution Marketing Rockstars
.
As Dell (the company that I work for) continues to service customers in all facets of OpenStack and Hadoop implementations, we are beginning another season of growth on the Revolutionary Solutions team.
The Dell Revolutionary Solutions Team delivers the Dell OpenStack-Powered Cloud Solution and the Dell Apache Hadoop Solution, leads the Crowbar open source project, and manages the Emerging Solutions Ecosystem Partner Program that includes a number of key partners such as Suse, Inktank, Cloudera, Datameer, and Pentaho.
We are looking for a variety of engineers and presales teams, but I will focus on the product management and marketing roles in this post.
Technical Product Managers – Product managers to be technical SMEs (roadmaps, requirements, etc) on partner products in the cloud and big data spaces, most notably OpenStack and Hadoop, but could also be focused on other emerging solutions spaces – Link to Job Posting
- Product Marketing Managers – Marketing experts to own and lead go-to-market strategy and deliverables in the cloud and big data spaces (marketing strategy, sales enablement, etc). Again, this would certainly cover our OpenStack and Hadoop solutions today, but could also focus on future emerging solutions spaces. – Link to Job Posting
- Open Source Community Manager / Evangelist – Community oriented professionals with strong networks, strong social media presence, and an ability to bring collaborators and customers together to work on common goals – Link to Job Posting
- Marketing Directors – Experienced people managers to drive business objectives, product vision, and go-to-market strategy, specifically in the areas of Product Management and Product Marketing – Link to Job Posting
In our experience, the best candidates
- have a track record of ownership
- have a techincal background
- are experienced in their discipline
- are participants in cloud, big data, virtualization, or similar emerging technologies
Pass it on to a friend or apply yourself – I look forward to hearing from you!
Until next time,
JBGeorge
@jbgeorge
NOW HIRING: Dell’s Revolutionary Cloud and Big Data Team Expands
.
We’re growing!
The Revolutionary Cloud and Big Data Team at Dell (the company I work for) is looking to expand our team of rockstars, so we’re putting the word out. Specifically we’re looking for architects, engineers, developers, and I’m looking to hire a few more senior product managers to join my team of subject matter experts.
Just for context, we’re the team that has taken to market the Dell OpenStack-Powered Cloud Solution, the Dell Apache Hadoop Solution, and the Dell Crowbar software framework and open source project.
And if you’re a rockstar in any of those spaces, we’d like to talk to you.
SPOILER ALERT – If you’re interested in talking to us about a technical spot on our team, you can email us your info and resume at OpenStack@Dell.com or Hadoop@Dell.com.
What is this team about?
A few years ago, the Dell Data Center Solutions team came into being with a mission of servicing the biggest hyperscale environments in the world, which included many of the market’s top cloud providers. It has succeeded in its mission in dominating the density optimized space (check out more on that here), and in fact, just shipped it’s ONE MILLIONTH SERVER.
An extension of DCS’s mission soon became clear – as many customers were looking to accelerate into spaces like cloud and big data, providing them integrated solutions would ease their implementation of these technologies. And so our Revolutionary Cloud and Big Data Solutions team was born – to deliver integrated solutions based on cutting edge technologies like OpenStack and Hadoop (and more), as well as innovative Dell projects like Crowbar, in an effort to enable customers to grow and thrive in their businesses with our products, innovation, and expertise.
Who are we?
The team at Dell is made up of a number of people, like myself, that you’d recognize from OpenStack and Hadoop circles – folks like Rob Hirschfeld, Greg Althaus, Kamesh Pemmaraju, and others. We all come from a variety of backgrounds – some from big companies in the technology spaces and many from startups – we happen to have quite a few entreprenuers on our team! And we try to service our customers in the best way possible – agile development processes, open source friendly, community oriented, etc.
What are we trying to do?
Our mission is to develop and deliver HW+SW+Services solutions to market that will enable our customers to be successful. Clear and simple.
Here’s a sampling of what our team has done over the course of our existence:
- The first hardware solutions vendor to support OpenStack
- Released the first HW+SW+Services OpenStack solution to market – the Dell OpenStack-Powered Cloud Solution
- Launch of open source project “Crowbar” to fill the void of an automated bare metal OpenStack provisioner
- Released HW+SW+Services Apache Hadoop solution to market – the Dell Apache Hadoop Solution
- Launch of the Emerging Solutions Ecosystem Partner Program to enable our customers by incorporating some of our best in breed partner technologies into our solutions, which includes Datameer, Pentaho, enStratus, Mirantis, and Canonical, with more to come
- Launch of the Emerging Solutions Platform Partner Program to enable our customers by delivering solutions focused on specific workloads and target markets
In addition, we’re big believers in the community – we regularly hold hackfests to help move these communities forward, lead community meetups in Austin and Boston working with other key vendors that co-sponsor with us (you may be surprised), are regularly active in IRC, skype discussions, conference breakout sessions, and more.
It’s a fast-paced, customer focused, ever evolving group and its a great place to deliver tanglible, difference making solutions to customers.
It’s not for the faint of heart, but it’s DEFINITELY for the mover and shaker.
Who we want to hear from
We’re looking to expand in a number of areas, but specifically we’re looking for technical talent
- Developers / QA
- Technical Product Managers and Strategists
- Architects and Technical Leads
If I’ve piqued your interest, drop me a note and your resume at OpenStack@Dell.com.
Look forward to hearing from cloud / big data / open source rockstars.
Until next time,
JOSEPH
@jbgeorge
The JBGeorge Tech Blog 