Home
Conference Info
Sponsorship Information
Speakers
Schedule
Exhibitors
Media Sponsors
Registration
Press Registration
  Topics
  Call For Papers
  Past Events
  Sessions
  Presentations
  Power Panels
  Videos
Untitled Document
2018 East Exhibitors

Untitled Document
2018 East Media Sponsors








Untitled Document
2017 West
Premium Sponsors
Diamond



Platinum
@DevOpsSummit

Bronze










Untitled Document
2017 West
Keynote Sponsor


Untitled Document
2017 West Exhibitors
























@ThingsExpo











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.

Testimonials
This week I had the pleasure of delivering the opening keynote at Cloud Expo New York. It was amazing to be back in the great city of New York with thousands of cloud enthusiasts eager to learn about the next step on their journey to embracing a cloud-first worldl."
@SteveMar_Msft
 
How does Cloud Expo do it every year? Another INCREDIBLE show - our heads are spinning - so fun and informative."
@SOASoftwareInc
 
Thank you @ThingsExpo for such a great event. All of the people we met over the past three days makes us confident IoT has a bright future."
@Cnnct2me
 
One of the best conferences we have attended in a while. Great job, Cloud Expo team! Keep it going."

@Flexential


Who Should Attend?
Senior Technologists including CIOs, CTOs & Vps of Technology, Chief Systems Engineers, IT Directors and Managers, Network and Storage Managers, Enterprise Architects, Communications and Networking Specialists, Directors of Infrastructure.

Business Executives including CEOs, CMOs, & CIOs , Presidents & SVPs, Directors of Business Development , Directors of IT Operations, Product and Purchasing Managers, IT Managers.

Join Us as a Media Partner - Together We Can Enable the Digital Transformation!
SYS-CON Media has a flourishing Media Partner program in which mutually beneficial promotion and benefits are arranged between our own leading Enterprise IT portals and events and those of our partners.

If you would like to participate, please provide us with details of your website/s and event/s or your organization and please include basic audience demographics as well as relevant metrics such as ave. page views per month.

To get involved, email [email protected].

Digital Transformation Blogs
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were repetitive. In addition, the paper-based process used to measure patient health metrics was laborious, not in real-time and sometimes error-prone. In their session at 21st Cloud Expo, Sean Finnerty, Executive Director, Practice Lead, Health Care & Life Science at REAN Cloud, and Dr. S.P.T. Krishnan, Principal Architect at REAN Cloud, discussed how they built an Alexa-powered voice application for both patients and nurses. Patients got answers for common qu...
When talking IoT we often focus on the devices, the sensors, the hardware itself. The new smart appliances, the new smart or self-driving cars (which are amalgamations of many ‘things'). When we are looking at the world of IoT, we should take a step back, look at the big picture. What value are these devices providing. IoT is not about the devices, its about the data consumed and generated. The devices are tools, mechanisms, conduits. This paper discusses the considerations when dealing with the massive amount of information associated with these devices. Ed presented sought out sessions at Cl...
Bill Schmarzo, author of "Big Data: Understanding How Data Powers Big Business" and "Big Data MBA: Driving Business Strategies with Data Science," is responsible for setting the strategy and defining the Big Data service offerings and capabilities for EMC Global Services Big Data Practice. As the CTO for the Big Data Practice, he is responsible for working with organizations to help them identify where and how to start their big data journeys. He's written several white papers, is an avid blogger and is a frequent speaker on the use of Big Data and data science to power the organization's key ...
DevOpsSUMMIT Blogs
When applications are hosted on servers, they produce immense quantities of logging data. Quality engineers should verify that apps are producing log data that is existent, correct, consumable, and complete. Otherwise, apps in production are not easily monitored, have issues that are difficult to detect, and cannot be corrected quickly. Tom Chavez presents the four steps that quality engineers should include in every test plan for apps that produce log output or other machine data. Learn the steps so your team's apps not only function but also can be monitored and understood from their machine...
Sanjeev Sharma Joins November 11-13, 2018 @DevOpsSummit at @CloudEXPO New York Faculty. Sanjeev Sharma is an internationally known DevOps and Cloud Transformation thought leader, technology executive, and author. Sanjeev's industry experience includes tenures as CTO, Technical Sales leader, and Cloud Architect leader. As an IBM Distinguished Engineer, Sanjeev is recognized at the highest levels of IBM's core of technical leaders.
Containers and Kubernetes allow for code portability across on-premise VMs, bare metal, or multiple cloud provider environments. Yet, despite this portability promise, developers may include configuration and application definitions that constrain or even eliminate application portability. In this session we'll describe best practices for "configuration as code" in a Kubernetes environment. We will demonstrate how a properly constructed containerized app can be deployed to both Amazon and Azure using the Kublr platform, and how Kubernetes objects, such as persistent volumes, ingress rules, and...
Presentation Slides
Wooed by the promise of faster innovation, lower TCO, and greater agility, businesses of every shape and size have embraced the cloud at eve...
Recently, REAN Cloud built a digital concierge for a North Carolina hospital that had observed that most patient call button questions were ...
CloudEXPO.TV
"Avere Systems deals with data performance optimization in the cloud or on-premise. Even to this day many organizations struggle with what we call the problem o...
"We began as LinuxAcademy.com about five years ago as a very small outfit. Since then we've transitioned into more of a DevOps training company - the technologi...