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Little Data, Big Data and Very Big Data (VBD) or Big BS?
I routinely hear from different people or groups trying to define what is or is not big data

This is an industry trends and perspective piece about big data and little data, industry adoption and customer deployment.

If you are in any way associated with information technology (IT), business, scientific, media and entertainment computing or related areas, you may have heard big data mentioned. Big data has been a popular buzzword bingo topic and term for a couple of years now. Big data is being used to describe new and emerging along with existing types of applications and information processing tools and techniques.

I routinely hear from different people or groups trying to define what is or is not big data and all too often those are based on a particular product, technology, service or application focus. Thus it should be no surprise that those trying to police what is or is not big data will often do so based on what their interest, sphere of influence, knowledge or experience and jobs depend on.

Traveling and big data images

Not long ago while out traveling I ran into a person who told me that big data is new data that did not exist just a few years ago. Turns out this person was involved in geology so I was surprised that somebody in that field was not aware of or working with geophysical, mapping, seismic and other legacy or traditional big data. Turns out this person was basing his statements on what he knew, heard, was told about or on sphere of influence around a particular technology, tool or approach.

Fwiw, if you have not figured out already, like cloud, virtualization and other technology enabling tools and techniques, I tend to take a pragmatic approach vs. becoming latched on to a particular bandwagon (for or against) per say.

Not surprisingly there is confusion and debate about what is or is not big data including if it only applies to new vs. existing and old data. As with any new technology, technique or buzzword bingo topic theme, various parties will try to place what is or is not under the definition to align with their needs, goals and preferences. This is the case with big data where you can routinely find proponents of Hadoop and Map reduce position big data as aligning with the capabilities and usage scenarios of those related technologies for business and other forms of analytics.

SAS software for big data

Not surprisingly the granddaddy of all business analytics, data science and statistic analysis number crunching is the Statistical Analysis Software (SAS) from the SAS Institute. If these types of technology solutions and their peers define what is big data then SAS (not to be confused with Serial Attached SCSI which can be found on the back-end of big data storage solutions) can be considered first generation big data analytics or Big Data 1.0 (BD1 ;) ). That means Hadoop Map Reduce is Big Data 2.0 (BD2 ;) ;) ) if you like, or dislike for that matter.

Funny thing about some fans and proponents or surrogates of BD2 is that they may have heard of BD1 like SAS with a limited understanding of what it is or how it is or can be used. When I worked in IT as a performance and capacity planning analyst focused on servers, storage, network hardware, software and applications I used SAS to crunch various data streams of event, activity and other data from diverse sources. This involved correlating data, running various analytic algorithms on the data to determine response times, availability, usage and other things in support of modeling, forecasting, tuning and trouble shooting. Hmm, sound like first generation big data analytics or Data Center Infrastructure Management (DCIM) and IT Service Management (ITSM) to anybody?

Now to be fair, comparing SAS, SPSS or any number of other BD1 generation tools to Hadoop and Map Reduce or BD2 second generation tools is like comparing apples to oranges, or apples to pears. Lets move on as there is much more to what is big data than simply focus around SAS or Hadoop.

StorageIO industry trends cloud, virtualization and big data

Another type of big data are the information generated, processed, stored and used by applications that result in large files, data sets or objects. Large file, objects or data sets include low resolution and high-definition photos, videos, audio, security and surveillance, geophysical mapping and seismic exploration among others. Then there are data warehouses where transactional data from databases gets moved to for analysis in systems such as those from Oracle, Teradata, Vertica or FX among others. Some of those other tools even play (or work) in both traditional e.g. BD1 and new or emerging BD2 worlds.

This is where some interesting discussions, debates or disagreements can occur between those who latch onto or want to keep big data associated with being something new and usually focused around their preferred tool or technology. What results from these types of debates or disagreements is a missed opportunity for organizations to realize that they might already be doing or using a form of big data and thus have a familiarity and comfort zone with it.

By having a familiarity or comfort zone vs. seeing big data as something new, different, hype or full of FUD (or BS), an organization can be comfortable with the term big data. Often after taking a step back and looking at big data beyond the hype or fud, the reaction is along the lines of, oh yeah, now we get it, sure, we are already doing something like that so lets take a look at some of the new tools and techniques to see how we can extend what we are doing.

Likewise many organizations are doing big bandwidth already and may not realize it thinking that is only what media and entertainment, government, technical or scientific computing, high performance computing or high productivity computing (HPC) does. I'm assuming that some of the big data and big bandwidth pundits will disagree, however if in your environment you are doing many large backups, archives, content distribution, or copying large amounts of data for different purposes that consume big bandwidth and need big bandwidth solutions.

Yes I know, that's apples to oranges and perhaps stretching the limits of what is or can be called big bandwidth based on somebody's definition, taxonomy or preference. Hopefully you get the point that there is diversity across various environments as well as types of data and applications, technologies, tools and techniques.

StorageIO industry trends cloud, virtualization and big data

What about little data then?

I often say that if big data is getting all the marketing dollars to generate industry adoption, then little data is generating all the revenue (and profit or margin) dollars by customer deployment. While tools and technologies related to Hadoop (or Haydoop if you are from HDS) are getting industry adoption attention (e.g. marketing dollars being spent) revenues from customer deployment are growing.

Where big data revenues are strongest for most vendors today are centered around solutions for hosting, storing, managing and protecting big files, big objects. These include scale out NAS solutions for large unstructured data like those from Amplidata, Cray, Dell, Data Direct Networks (DDN), EMC (e.g. Isilon), HP X9000 (IBRIX), IBM SONAS, NetApp, Oracle and Xyratex among others. Then there flexible converged compute storage platforms optimized for analytics and running different software tools such as those from EMC (Greenplum), IBM (Netezza), NetApp (via partnerships) or Oracle among others that can be used for different purposes in addition to supporting Hadoop and Map reduce.

If little data is databases and things not generally lumped into the big data bucket, and if you think or perceive big data only to be Hadoop map reduce based data, then does that mean all the large unstructured non little data is then very big data or VBD?

StorageIO industry trends cloud, virtualization and big data

Of course the virtualization folks might want to if they have not already corner the V for Virtual Big Data. In that case, then instead of Very Big Data, how about very very Big Data (vvBD). How about Ultra-Large Big Data (ULBD), or High-Revenue Big Data (HRBD), granted the HR might cause some to think its unique for Health Records, or Human Resources, both btw leverage different forms of big data regardless of what you see or think big data is.

Does that then mean we should really be calling videos, audio, PACs, seismic, security surveillance video and related data to be VBD? Would this further confuse the market, or the industry or help elevate it to a grander status in terms of size (data file or object capacity, bandwidth, market size and application usage, market revenue and so forth)?

Do we need various industry consortiums, lobbyists or trade groups to go off and create models, taxonomies, standards and dictionaries based on their constituents needs and would they align with those of the customers, after all, there are big dollars flowing around big data industry adoption (marketing).

StorageIO industry trends cloud, virtualization and big data

What does this all mean?

Is Big Data BS?

First let me be clear, big data is not BS, however there is a lot of BS marketing BS by some along with hype and fud adding to the confusion and chaos, perhaps even missed opportunities. Keep in mind that in chaos and confusion there can be opportunity for some.

IMHO big data is real.

There are different variations, use cases and types of products, technologies and services that fall under the big data umbrella. That does not mean everything can or should fall under the big data umbrella as there is also little data.

What this all means is that there are different types of applications for various industries that have big and little data, virtual and very big data from videos, photos, images, audio, documents and more.

Big data is a big buzzword bingo term these days with vendor marketing big dollars being applied so no surprise the buzz, hype, fud and more.

Ok, nuff said, for now...

Cheers Gs

Greg Schulz - Author Cloud and Virtual Data Storage Networking (CRC Press, 2011), The Green and Virtual Data Center (CRC Press, 2009), and Resilient Storage Networks (Elsevier, 2004)

twitter @storageio

All Comments, (C) and (TM) belong to their owners/posters, Other content (C) Copyright 2006-2012 StorageIO All Rights Reserved

Read the original blog entry...

About Greg Schulz
Greg Schulz is founder of the Server and StorageIO (StorageIO) Group, an IT industry analyst and consultancy firm. Greg has worked with various server operating systems along with storage and networking software tools, hardware and services. Greg has worked as a programmer, systems administrator, disaster recovery consultant, and storage and capacity planner for various IT organizations. He has worked for various vendors before joining an industry analyst firm and later forming StorageIO.

In addition to his analyst and consulting research duties, Schulz has published over a thousand articles, tips, reports and white papers and is a sought after popular speaker at events around the world. Greg is also author of the books Resilient Storage Network (Elsevier) and The Green and Virtual Data Center (CRC). His blog is at www.storageioblog.com and he can also be found on twitter @storageio.

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Cloud Expo - Cloud Looms Large on SYS-CON.TV


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With our launch at Cloud Expo, we successfully transformed the company from a relatively unknown European player into the dominant player in the market. Our competitors were taken by surprise and just blown away. We got a huge number of really high quality leads..."
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Latest Blog Posts
The cloud is everywhere and growing, and with it SaaS has become an accepted means for software delivery. SaaS is more than just a technology, it is a thriving business model estimated to be worth around $53 billion dollars by 2015, according to IDC. The question is – how do you build and scale a profitable SaaS business model? In his session at 15th Cloud Expo, Jason Cumberland, Vice President, SaaS Solutions at Dimension Data, will give the audience an understanding of common mistakes businesses make when transitioning to SaaS; how to avoid them; and how to build a profitable and scalable SaaS business.

Seagate has shipped over 10 Million storage HHDD's, is that a lot?

By Greg Schulz

Seagate has shipped over 10 Million storage HHDD's, is that a lot?

Recently Seagate made an announcement that they have shipped over 10 million Hybrid Hard Disk Drives (HHDD) also known as Solid State Hybrid Drives (SSHD) over that past few years. Disclosure Seagate has been a StorageIO client.

I know where some of those desktop class HHDD's including Momentus XTs ended up as I bought some of the 500GB and 750GB models via Amazon and have them in various systems. Likewise I have installed in VMware servers the newer generation of enterprise class SSHD'...

Docker for Smarties (e.g. non-dummies) from VMworld 2014

By Greg Schulz

Docker for Smarties (e.g. non-dummies) from VMworld 2014

In this Industry Trends Perspectives video pod cast episode (On YouTube) I had a chance to visit with Nathan LeClaire of docker.com at the recent VMworld 2014 in San Francisco for a quick overview of docker and containers are about, what you need to know and where to find more information. Check out this StorageIO Industry Trends Perspective episode "Docker for Smarties" aka not for dummies via YouTube by clicking here or on the image below.

Sep. 22, 2014 12:00 PM EDT  Reads: 825
So exactly how do you kick start a DevOps strategy? For example, say your organization is tied down to a very sequential, but cumbersome Waterfall approach to software development that is wasting precious dollars and hindering productivity? In the following we’ve outlined some strategy tips that every business leader will need to consider as they start down the path of DevOps adoption. Whatever steps your organization takes on the DevOps path of rolling out software faster and more effectively and deployment will require the support of your senior level management team. Explain the advantages of DevOps to the executive team in terms that they can easily understand. Provide an outline of how DevOps and cloud computing can save on ROI and get your new mobile application into the hands of the customer faster and more effectively with higher quality.
Amazon is indisputably the biggest name in cloud service providers. They have built up a strong market presence primarily on the argument that access to cheap compute and storage resources is attractive to companies looking to shed IT costs as they move from on-premises solutions to the cloud. But after the initial push for cheap resources, how will this market develop? Amazon has cut prices to their cloud offering more than 40 times since introducing the service in 2006. The way this gets translated in press circles is that cloud services pricing is approaching some floor. But is that true?
When it comes to cloud storage services, the options are plentiful. For personal or business use, cloud storage has quickly become a vital element of saving important information, whether they be company files, personal photos, or documents. In fact, many businesses plan out strategies that largely depend on the cloud services they use. With that in mind, the choice of cloud storage provider has become an immensely important one. Many different choices exist, some offered by large corporations while others comes from startups. To differentiate what service is best for you, it's best to focus on the features each one offers. Not every cloud storage service is built the same, so a careful examination is in order.
It's hard to miss the world of opportunities that data collection and analysis have opened up. But how can you avoid having information overload? It takes a lot of will power, in our data obsessed world to say "too much!" However, there are many ways where too much information is destroying productivity, and actually causing bad decision making, not good. But it is hard to avoid the world of opportunities that has been opened in data collection and analysis. So how do you balance the two? The first step is to understand there is a big difference between data collection, and it's utilization. While it seems subtle, the difference is key, and utilization is where many make mistakes.
Senior executives at large multinational enterprises are already demanding that their CIO has a plan in place to ensure that they can effectively procure public and private cloud services for their organization. In smaller companies, some IT managers are now expected to acquire the knowledge and skills to perform a similar role. Are they prepared? To find out, let's review a current IT resource assessment.
Over the past couple of days Platform-as-a-Service (PaaS) has taken some hard knocks in the press. See here and here. PaaS has always had a hard life. It’s typical middle child syndrome. It’s older sibling SaaS is very mature and is growing considerably everyday thanks to the adoption by line-of-business leaders. It’s younger sibling, IaaS, gets all the attention from the uber geeks who prefer to manage everything themselves. Poor PaaS is left trying to wring out an identity for itself; some unique value that users can grasp onto. Unfortunately, the support PaaS needs to come into its own is hard to come by these days.
We (as in the industry at large) don't talk enough about applying architectural best practices with respect to emerging API and software-defined models of networking. But we should. That's because as we continue down the path that continues to software-define the network, using APIs and software development methodologies to simplify and speed the provisioning of network services, the more we run into if not rules, then best practices, that should be considered before we willy nilly start integrating all the network things.
The keys to the digital kingdom are credentials. In no industry is this more true (and ultimately more damaging) than financial services. The sophistication of the attacks used to gather those credentials and thwart the increasingly complex authentication process that guards financial transactions is sometimes staggering. That's because they require not just stealth but coordination across multiple touch points in the transaction process.

When you plan your migration to the cloud, and the cloud security best practices to secure it, there is no need to reinvent the wheel.  Here is some advice from the Fortune 500. Use these tips to learn from others’ successes and to avoid their failures – maybe their companies can afford “valuable” learning lessons, […]

The post Cloud Security Best Practices of the Fortune 500 appeared first on Porticor Cloud Security.

The global village, mobile devices, online marketplaces, social networks, and on-demand entertainment all have a part to play. People all over the world are increasing the time they spend in the virtual world. They’re buying, selling, sharing, studying, developing apps, hanging out in social networks, and starting to use digital currencies that bypass traditional banking. Alongside these community-driven ideas, we are also seeing enormous change in business to business relationships. Cloud computing enables any size business to obtain and manage big-business manufacturing, warehousing, marketing, data analytics, enterprise applications and global spread. Supply chains are radically altered: a business of any size can buy, produce and sell globally, and leverage vertically and horizontally integrated supply chains.

#SDAS #IAM #IoT #Mobile The new requirements for app delivery include a focus on hyperscaling access to applications.

A plurality (48%) of enterprises deliver between 1 and 500 applications to consumers and employees. A somewhat surprising 21% deliver more than 1000 applications every day*.

Consider, now, the possible combinations (or is it permutations, I always mix those two up) that can be formed along with the increasing number of devices/connections per consumer and employee (predicted to hit 5 per individual by 2017 by Cisco). Oh, and don't forget to consider the potential impact from the Internet of Things. Things that need access to applications and data controlled by corporate access policies.

As you've probably already surmised, traditional access control technology isn't going to scale well in the face of that many potential entry points into the organization. In many cases, even modern access control solutions aren't going to scale - operationally or ...

The Internet has changed the way businesses are constructed: vertical integrations and home-grown systems are being steadily replaced by off-the-shelf solutions, SaaS integrations, and web-based workflows. Documents are no longer stored on a file server halfheartedly maintained by your IT department, they’re centralized in a document-storage site like Dropbox or Box.com. Productivity software is no longer something that lives on your desktop computer in your office, but rather on the cloud using Google Drive or Office365.
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