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Published: Wed, 17 Jan 2018 07:26:40 PST

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There's No Place Like a Smart Home

Tue, 09 Jan 2018 21:59:59 PST

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We’ve been using technology to make our homes more comfortable, more secure and more entertaining for decades – with varying degrees of success. In this episode, we’ll discuss a history of smart technologies used inside the house, and analyze just how smart they really could be.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/8005800/839a7be9.mp3?CID=311880

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Toy to the World

Tue, 05 Dec 2017 21:59:59 PST

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The toy industry has a long history, but only really became a technology-heavy omnipresence since the 1970s. In this episode, see how toy manufacturers have adapted digital – or not – to win the battle for children’s attention.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/7632039/5d8cd2df.mp3?CID=311880

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Architectural Tenets of Deep Learning

Wed, 29 Nov 2017 14:30:04 PST

Lately, I have spent large swaths of my time focused around Deep Learning and Neural Networks (either with customers or in our lab).   One of the most common questions that I get is around underperforming model training with regard to “wall clock time”.  This has more to do with focusing on only one aspect of their architecture, say GPUs. As such, I will spend a little time writing about the 3 fundamental tenets for a successful Deep Learning architecture.  These fundamental tenants are compute, file access, and bandwidth. Hopefully this will resonate and help provide some thoughts for those customers on their journey. Overview Deep Learning (DL) is certainly all the rage. We are defining DL as a type of Machine Learning (ML) built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network”. The use cases that I am presented with continue to grow exponentially with very compelling financial return on investments. Whether it is Convolutional Neural Networks (CNNs) for Computer Vision or Recurrent Neural Networks (RNNs) for Natural Language Processing (NLP) or Deep Belief Networks (DBN) for Restricted Boltzmann Machines (RBMs), Deep Learning has many architectural structures and acronyms. There is some great Neural Network information out there.  Pic 1 is a good representation of the structural layers for Deep Learning on Neural Networks:   Pic 1 Orchestration Orchestration tools like BlueData, Kubernetes, Mesosphere, or Spark Cluster Manager are the top of the layer cake of implementing Deep Learning with Neural Networks.  These provide scheduler and possibility container capabilities to the stack.  This layer is the most visible to the Operations team running the Deep Learning environment.  There are certainly pros and cons to the different orchestration layers, but that is a topic for another blog. Deep Learning Frameworks Caffe2, CNTK, Pikachu, PyTorch, or Torch.  One of these is a cartoon game character.  The rest sound like they could be in a game, but they are some of the blossoming frameworks that support Deep Learning with Neural Networks.  Each framework has their pros and cons with different training libraries and different neural networks structures (s) for different use cases.  I regularly see a mix of frameworks within Deep Learning environments and the Framework chosen rarely changes the 3 tenets for architecture. Architectural Tenets I’ll use an illustrative use case to highlight the roles of the architectural tenets below.  Since the Automotive industry has Advanced Driving (ADAS) and Financial Services have Trader Surveillance use cases, we will explore a CNN with Computer Vision.  Assume a 16K resolution image that stores around 1 gigabyte (GB) in a file on storage and has 132.7 million pixels. Compute To dig right in, the first architectural tenet is Compute.   The need for compute is one of those self-obvious elements of Deep Learning.  Whether you use GPU, CPU, or a mix tends to result from which neural network structure (CNNs vs RNNs vs DBNs), use cases, or preferences.  The internet is littered with benchmarks postulating CPUs vs GPUs for different structures and models.  GPUs are the mainstay that I regularly see for Deep Learning on Neural Networks, but each organization has their own preferences based upon past experiences, budget, data center space, and network layout.  The overwhelming DL need for Compute is for lots of it. If we examine our use case of the 16K image, the CNN will dictate how the image is addressed.  The Convolutional Layer or the first layer of a CNN will parse out the pixels for analysis.  132.7M pixels will be fed to 132.7M different threads for processing.  Each compute thread will create an activation map or feature map that helps to weight the remaining CNN layers.  Since this volume of threads for a single job is rather large[...]



Live and in Concert

Tue, 28 Nov 2017 21:59:59 PST

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From amphitheaters to theme parks, from dairy farms to airports, the live event industry is constantly finding new ways, and new places to bring us together. And this episode is your backstage pass to hear how it all works.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/7554146/779275f2.mp3?CID=311880

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Untethered Mixed Reality... On the Edge

Tue, 28 Nov 2017 04:59:59 PST

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“People care about the technology and tools in their workplace more than any other perks.” Sam Burd, who leads Dell’s Client Solutions Group, takes the research behind this statement very seriously and, as an 18 year veteran at Dell, knows that constant innovation, research and disruption is indispensable when working in IT. Putting the smartest devices into the hands of the workforce is not only beneficial in the context of vying for the best talent, but also from the perspective of having the most creative, motivated and productive workforce. Sam believes that the opportunity that exists on the edge with technology to build great devices is greater than ever, but also more complex than it has ever been. Listen to Sam’s conversation with Mark Schaefer and Doug Karr where virtual and mixed reality as well as the demands of the workforce of the future are hot topics.e?

ENCLOSURE:http://tracking.feedpress.it/link/16994/7390011/4aed1e09.mp3?CID=311880

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Democratizing Artificial Intelligence, Deep Learning and Machine Learning with Dell EMC Ready Solutions

Mon, 20 Nov 2017 14:57:31 PST

Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) are at the heart of digital transformation by enabling organizations to exploit their growing wealth of big data to optimize key business and operational use cases. • AI is the theory and development of computer systems able to perform tasks normally requiring human intelligence (e.g. visual perception, speech recognition, translation between languages, etc.). • ML is a sub-field of AI that provides systems the ability to learn and improve by itself from experience without being explicitly programmed. • DL is a type of ML built on a deep hierarchy of layers, with each layer solving different pieces of a complex problem. These layers are interconnected into a “neural network.” A DL framework is SW that accelerates the development and deployment of these models. See “Artificial Intelligence is not Fake Intelligence” for more details on AI | ML | DL. And the business ramifications are staggering (see Figure 1)! Figure 1: Source : McKinsey And Senior Executives seem to have gotten the word.  BusinessWeek (October 23, 2017) reported a dramatic increase in mentions of  “artificial intelligence” during 363 third quarter earnings calls (see Figure 2). Figure 2: Executives Mentioning “Artificial Intelligence” During Earnings Calls To help our clients exploit the business and operational benefits of AI | ML | DL, Dell EMC has created “Ready Bundles” that are designed to simplify the configuration, deployment and management of AI | ML | DL solutions.  Each bundle includes integrated servers, storage, networking as well as DL and ML frameworks (such as TensorFlow, Caffe, Neon, Intel BigDL, Intel Nervana Deep Learning Studio, Intel Math Kernel Library-Deep Neural Networks, and Intel Machine Learning Scaling Library) for optimized ML or deep learning. Driving AI | ML | DL Democratization Democratization is defined as the action/development of making something accessible to everyone, to the “common masses.”  History provides democratization lessons from the Industrial and Information Revolutions.  Both of these moments in history were driven by the standardization of parts, tools, architectures, interfaces, designs and trainings that allowed for the creation of common platforms.  Instead of being dependent upon a “high priesthood” of specialists to assemble your guns or cars or computer systems, organizations of all sizes where able to leverage common platforms to build their own sources of customer, business and financial differentiation. AI | ML | DL technology stacks are complicated systems to tune and maintain, expertise is limited, and one minimal change of the stack can lead to failure.  The AI | ML | DL market needs to go through a similar “standardization” process in order to create AI | ML | DL platforms that enable organizations of all sizes to build their own sources of customer, business and financial differentiation. To help accelerate AI | ML | DL democratization, Dell EMC has created Machine Learning and Deep Learning Ready Bundles.  These pre-packaged Ready Bundles de-risk and simplify AI | ML | DL projects and accelerate time-to-value by pre-integrating the necessary hardware and software. No longer is a siloed knowledge group of specialists required to stand up your AI | ML | DL environments.  Instead, organizations can focus their valuable data engineering and data science resources on creating new sources of customer, business and operational value. Monetizing Machine Learning with Dell EMC Consulting Across every industry, organizations are moving aggressively to adopt AI | ML | DL tools and frameworks to help them become more effective in leveraging data and analytics to power their key business and operational use cases (see Figure 3). Figure 3: AI | ML | DL Use Cases Across Industries T[...]



Knock Knock: Special Delivery

Tue, 14 Nov 2017 21:59:59 PST

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What used to take a month, now takes hours. Delivering anywhere in the country is becoming faster and cheaper than ever. In this episode, we’ll take a look at the steps we took to get closer and closer to instant gratification.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/7403904/8c6077a3.mp3?CID=311880

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Scientific Method: Embrace the Art of Failure

Thu, 02 Nov 2017 14:53:04 PDT

I use the phrase “fail fast / learn faster” to describe the iterative nature of the data science exploration, testing and validation process.  In order to create the “right” analytic models, the data science team will go through multiple iterations testing different variables, different data transformations, different data enrichments and different analytic algorithms until they have failed enough times to feel “comfortable” with the model that they have developed. However an early variant of this process has been employed a long time: it’s called the Scientific Method. The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. To be termed scientific, a method of inquiry is commonly based on empirical or measurable evidence subject to specific principles of reasoning[1] (see Figure 1). Figure 1: The Scientific Method The Scientific Method is comprised of the following components: Process: The overall process involves making hypotheses, deriving predictions from them as logical consequences, and then carrying out experiments based on those predictions to determine whether the original hypothesis was correct. Hypothesis Formulation: This stage involves finding and evaluating evidence from previous experiments, personal observations, and the work of other scientists. Sufficient time and effort should be invested in formulating the “right” hypothesis, as it will directly impact the process and potential outcomes. Hypothesis: Terms commonly associated with statistical hypotheses are null hypothesis and alternative hypothesis. A null hypothesis is the conjecture that the statistical hypothesis is false. Researchers normally want to show that the null hypothesis is false. The alternative hypothesis is the desired outcome.  Hypothesis testing is confusing because it’s a proof by contradiction. For example, if you want to prove that a clinical treatment has an effect, you start by assuming there are no treatment effects—the null hypothesis. You assume the null and use it to calculate a p-value (the probability of measuring a treatment effect at least as strong as what was observed, given that there are no treatment effects). A small p-value is a contradiction to the assumption that the null is true; that is, it casts doubt on the null hypothesis[2]. Prediction: This step involves creating multiple predictions (predictive models) using different data sources and analytic algorithms in order to determine the logical consequences of the hypothesis. One or more predictions are selected for further testing in the next step. Testing: The purpose of an experiment is to determine whether observations of the real world agree with or conflict with the predictions derived from the hypothesis. If the observations agree, confidence in the hypothesis increases; otherwise, confidence decreases. Analysis: This involves analyzing the results of the testing to determine next testing steps and/or actions. Data Science Engagement Process We have expanded upon the Scientific Method to take advantage of new data science technologies (e.g., machine learning, neural networks, reinforcement learning) and new big data capabilities (e.g., Hadoop, data lake, elastic data platform). Our data science team uses the Data Science engagement process outlined in Figure 2 to identify those variables and metrics that might be better predictors of performance. Figure 2: Data Science Engagement Process Our data science engagement process is comprised of the below steps: Define Hypothesis. Step 1 identifies and validates the prediction or hypothesis to test.  The hypothesis is created in collaboration with the business users to understand the sources of business differentiation (e.g., how the organ[...]



Clean Up On Aisle 9

Tue, 31 Oct 2017 21:59:59 PDT

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For 100 years, the supermarket has been the place to go to purchase groceries –but that’s rapidly changing and will almost certainly not be the case in the coming decade. Here, we look back on its reign at the top, and look ahead to its very precarious future.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/7262169/2e1b7a08.mp3?CID=311880

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Connecting Intelligent Things... With Your IT

Wed, 25 Oct 2017 04:59:59 PDT

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Learn how Dell’s CTO, Liam Quinn, sees the future of the Internet of Intelligent Things, IoT, augmented reality, virtual reality and artificial intelligence leveraging new technological developments from edge to core to cloud.

Mr. Quinn joined Dell in Jan 1997, and holds over 90 granted and pending patents, and was named inventor for the year in 2005, 2007, and 2014. Mr. Quinn represents Dell on the Board of the Wi-Fi Alliance, and the UT WNCG Board (Wireless Networking and Communications Group). He is the Dell Exec Sponsor for the UT Cockrell School of Engineering and the UT College of Natural Science. He is also a member of the UT Cockrell School of Engineering Advisory Board.

In this role Liam has responsibility to lead technology innovation across the client product groups and drive alignment across the Dell CTO organizations. In his current role, Liam is leading the Technology and architecture strategy for The Internet of Things, IP development and relevant standards participation, and the interlock process of client architecture with Enterprise, Software, and Services to deliver end-to-end system solutions for our key customer segments.

ENCLOSURE:http://tracking.feedpress.it/link/16994/7212198/c215a857.mp3?CID=311880

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Da Vinci: Il Grande Apripista // The Great Trailblazer

Tue, 17 Oct 2017 00:00:59 PDT

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There have been many Trailblazers throughout history. But perhaps none has loomed larger, or longer, than Leonardo da Vinci. From art, to science, to math, to technology, hear just how disruptive his contributions were—and still are—in this special episode.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/7110057/263192db.mp3?CID=311880

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Insights that crack the code

Sun, 01 Oct 2017 17:29:24 PDT

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The fight against rare disease is winnable. See how TGen is turning the tide with Dell Technologies solutions.
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Where Disruption Takes Flight

Tue, 26 Sep 2017 21:49:59 PDT

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The air travel industry has seen more than its share of turbulence in the century since its inception. Come fly with us on this wild ride through the friendly skies.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6924575/ceb1be24.mp3?CID=311880

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Disruption by design

Wed, 20 Sep 2017 15:56:43 PDT

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The forward thinkers at BIG ideas are changing the way we move and live with the power of Dell Technologies.
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Smart golf balls can really entertain

Wed, 20 Sep 2017 07:56:41 PDT

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How do you make a game that can turn joy into frustration in less than a minute a lot more fun? Topgolf uses their smart golf balls and Dell EMC servers to create entertainment for all.
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Better treatments start with better insights

Wed, 20 Sep 2017 07:56:39 PDT

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The leading health care provider in Massachusetts uses a data lake to accelerate medical research.
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Dairy farmer’s prediction about cow comes true 

Wed, 20 Sep 2017 07:56:37 PDT

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Cows that communicate? Dell Technologies makes it real. We help Chitale Dairy transform their day-to-day operations to the point where they are turning dairy farming into a high-tech industry.
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Driving innovation further

Wed, 20 Sep 2017 07:56:35 PDT

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From swing analysis to advanced fitting, Callaway capitalizes on vast amounts of data to bring out each player's best.
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Get even more choice with the Ready Bundle for Hortonworks with Isilon

Tue, 19 Sep 2017 06:30:01 PDT

Earlier this month marked the 1 year anniversary of Dell Technologies and the coming together of Dell and EMC. Looking back, it has truly been a great year with a lot of bright spots to reflect on. I am most excited about how we have been able to bring together two powerful product portfolios to create choice and value through unique solutions we now build for our customers. This can be seen across the company as our new portfolio drives increased opportunities to meet specific customer needs like creating more value add solutions for specific workloads. As a data analytics junkie, one that is near and dear to my heart is the recently released Dell EMC Ready Bundle for Hortonworks with Isilon Shared Storage. You might ask “Why is this so important”? First, this is a Ready Bundle and a part of the Ready Solutions family meaning you reduce your deployment risks and speed up your time in value. If you aren’t sure what Ready Solutions are then here is a Whitepaper from IDG.  Secondly, this new Ready Bundle with Isilon extends flexibility for the user more than ever before. As a heritage Dell offering, the Dell EMC Ready Bundles for Hadoop have been around for years but traditionally they have been designed on PowerEdge servers. When you needed to scale your environment you would need to scale both compute and storage together; not a bad thing for many customers and deployments of these Ready Bundles have been outstanding. Now however, with heritage EMC’s Isilon added to the Ready Solution cadre of technologies, we offer organization the choice to decouple storage from compute and scale independently these two distinct components while delivering world class data services that have earned Isilon the top spot on Gartner’s Magic Quadrant for Scale-Out File and Object storage. We generally find this is a great option for Hadoop deployments where capacity requirements are growing much more rapidly than processing requirements. In addition to the increased choice and data services that you get with Isilon, you still enjoy all of the benefits of the other Ready Solutions for Hortonworks Hadoop. This solution has been tested and validated for Hortonworks HDP by both Dell EMC and Hortonworks. Dell EMC and Hortonworks have continued to strengthen their partnership over the years and this is yet another example of how we have come together to provide a unique, integrated solution to meet customers’ needs. Both Dell EMC and Hortonworks are excited about how this new Ready Bundle will help drive even more business outcomes with customers achieving success with Hadoop much more quickly. Jeff Schmitt, Hortonworks’ Sr. Director of Channels and Alliances had this to say about the Ready Bundle “The Ready Bundle for Hortonworks is yet another example of joint Dell EMC and Hortonworks investment bringing increased value to customers. As HDP deployments continue to grow in scale, offering customers choice in their infrastructure deployments is critical. The Ready Bundle for Hortonworks provides a customer simplified deployment while allowing storage and compute to scale independently.” This new Ready Bundle release is the epitome of the value that this merger has created. If you find yourself having to scale your Hadoop environment to meet capacity needs or are looking where to start on your Hadoop journey, the Dell EMC Ready Bundle for Hortonworks Hadoop with Isilon is a great fit. Here is the Ready Bundle Solution Overview for you to learn more about this great solution.  [...]



Your IT... Hyperconverged

Wed, 13 Sep 2017 04:59:59 PDT

Trey Layton got his start in the military as an imagery analyst working for General Schwarzkopf during Desert Storm. His special skills of interpreting images led to him finding the Blackhawk helicopters downed during the Battle of Mogadishu, known as the ‘Blackhawk Down’ incident. The technology to transfer images from the receive location to the image analysis platform in the 1990’s was vastly different from what is possible today. Understanding the challenges, Trey built some of the first computer systems and networks specifically addressing this problem. The seed for a career in IT to solve customer challenges was sown. For Trey, it has never been about providing something cheaper, but rather about providing something different that solves the challenge. In other words, it’s not about getting new IT equipment, it’s about modernizing and assembling equipment to transform operations for speed and agility that frees resources for investment in other areas, such as giving IT staff back their weekends. Transformation starts in helping customers understand what they have, how that needs to change and adapt so they can accelerate and get escape velocity on that change. Converged and hyperconverged infrastructures with codified integration, baked-in operational experience and based on ruthless standardization to meet customers’ needs are core to this IT transformation. Related Content: How New IT Models Are Changing The IT Skills Landscape And Internal IT - The data center is in a period of rapid IT transformation as businesses are increasingly seeking competitive advantages in this digital era. At the center of these IT transformations lie converged infrastructure (CI), hyper-converged infrastructure (HCI) and software defined environments. Transform your Business with HCI - Hyperconverged infrastructure (HCI) is becoming a popular architecture choice, particularly for businesses consolidating infrastructure as part of a hybrid IT strategy to extend compute and storage resources outside the enterprise. At its current level of progress, HCI could soon become a foundation layer for the next generation of infrastructure at enterprises, midsized companies and remote deployments. Hybrid cloud is the next frontier for HCI, with most players looking to develop cloud orchestration and workload-migration capabilities to become hybrid cloud enablers for enterprises and service provider Hyper-Converged Infrastructure (HCI) Platform Diversity Leads to Strength in Numbers When all you have in your toolkit is a single type of hammer, it’s little wonder that every workload starts to look like the same proverbial nail. That’s the case with vendors that give IT organizations the choice of only one hyper-converged infrastructure (HCI) platform. They may have integrated compute and data storage onto a single platform, but, as is often the case with any platform, the devil is in the details. ENCLOSURE:http://tracking.feedpress.it/link/16994/6774469/36f28dcd.mp3?CID=311880 [...]



Cloudy with a Chance of Disruption

Tue, 12 Sep 2017 21:59:59 PDT

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A climate of disruption is constantly changing how we watch the weather. In this episode, Tim Gasparrini, Janice Stillman, Peter Moore, Sarah Witman, Jim Cantore, Cameron Clayton and Paul Roebber open the almanac and see what’s in the forecast.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6771395/199d6662.mp3?CID=311880

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The Road to Disruption

Tue, 29 Aug 2017 21:44:59 PDT

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From the Model T to the Model 3, the auto industry’s come a long way – and its most exciting era is yet to come. Join Walter and guests Matt Anderson, Ashlee Vance, Bryan Salesky, Oliver Cameron, and Hans-Werner Kaas as they discuss the long and winding road from horses to autonomous cars.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6624821/ae268fc7.mp3?CID=311880

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How Schema On Read vs. Schema On Write Started It All

Mon, 21 Aug 2017 05:00:07 PDT

Article originally appeared as Schema On Read vs. Schema On Write Explained. What’s the difference between Schema on read vs. Schema on write? How did Schema on read shift the way data is stored? Since the inception of Relational Databases in the 70’s, schema on write has be the defacto procedure for storing data to be analyzed. However recently there has been a shift to use a schema on read approach, which has led to the exploding popularity of Big Data platforms and NoSQL databases. In this post let’s take a deep dive into what are the differences between schema on read vs. schema on write. What is Schema On Write Schema on write is defined as creating a schema for data before writing into the database. If you have done any kind of development with a database you understand the structured nature of Relational Database(RDBMS) because you have used Structured Query Language (SQL) to read data from the database. One of the most time consuming task in a RDBMS  is doing Extract Transform Load (ETL) work. Remember just because the data is structured doesn’t mean it starts out that way. Most of the data that exist is in an unstructured fashion. Not only do you have to define the schema for the data but you must also structure it based on that schema. For example if I wanted to store menu data for a local restaurant how would I begin to set the schema and write the data into the database? First task is to setup the tables Item Ingredients Nutritional values Next index items to map relationships Then write a regular expression to extract fields for each table in the database Lastly write SQL insert statements for extracted data All those steps had to be done before being able to store the data and analyze it for new insights. The overhead for having to do the ETL is one of the reasons new data sets are hard to get into your Enterprise Data Warehouse(EDW) quickly. What is Schema On Read Schema on read differs from schema on write because you create the schema only when reading the data. Structured is applied to the data only when it’s read, this allows unstructured data to be stored in the database. Since it’s not necessary to define the schema before storing the data it makes it easier to bring in new data sources on the fly. The exploding growth of unstructured data and overhead of ETL for storing data in RDBMS is the main reason for shift to schema on read. Many times analyst aren’t sure what types of insights they will gain from new data sources which is why getting new data source is time consuming. Remember back to our schema on write scenario let’s walk through it using schema on read. First step is to load our data into the database Boom! We are done! All of the menu data is in the database. Any insights we want to investigate we can try and apply the schema while testing. Let’s be clear though, we are still doing ETL on the data to fit into a schema but only when reading the data. Think of this as schema on demand! Key Differences Schema On Read vs. Schema On Write Since schema on read allows for data to be inserted without applying a schema should it become the defacto database? No, there are pros and cons for schema on read and schema on write. For example when structure of the data is known schema on write is perfect because it can return results quickly. See the comparison below for a quick[...]



Smart IT Connects & Protects... Your Data

Wed, 16 Aug 2017 04:59:59 PDT

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Rooted in the mindset of an engineer, a scientist, a business development expert and an entrepreneur, Dr. Orna Berry analyzes business challenges from multiple angles to ensure that the IT and services solution achieves all objectives. Due north for all activities is customer experience. At the highest level, IT must connect data for analysis while ensuring its protection from loss and compromise, in any way, when it is stationary and when it is in motion.

This deceivingly simple premise requires extensive planning around what services need to be provided or automated, the right data to be collected, the right technologies, such as hybrid enterprise cloud and converged infrastructure to achieve scale and speed to market, and procurement within the confinements of the customer's budget and, of course, industry and government regulations. The latter is of particular relevance in the fintech vertical, as Dr. Berry points out. Add the advent of the Internet of Things and with it the distribution of wisdom to even more devices, the challenges to be solved multiply. The collaboration and trust between a customer and a vendor in co-developing their IT environment infrastructure is indispensable.

This episode also examines Dr. Berry's perspective on the impact of technology on future generations and, as Israel's first woman director of industrial research, the role of women in technology.

ENCLOSURE:http://tracking.feedpress.it/link/16994/6510064/537047e3.mp3?CID=311880

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Side Effects Include Disruption

Tue, 15 Aug 2017 21:29:59 PDT

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From X-Rays to wearables, better healthcare means peering deeper into the human body. Join Walter and guests Dr. Daniel Kraft, Reenita Das, Dr. Jeffrey Trent, Anne Wojcicki and Dr. Giselle Sholler as they explore how wellness is becoming more personal, and more proactive.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6507231/31976d3b.mp3?CID=311880

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Architecture Changes in a Bound vs. Unbound Data World

Mon, 14 Aug 2017 05:00:22 PDT

Originally posted as Bound vs. Unbound Data in Real Time Analytics. Breaking The World of Processing Streaming and Real-Time analytics are pushing the boundaries of our analytic architecture patterns. In the big data community we now break down analytics processing into batch or streaming. If you glance at the top contributions most of the excitement is on the streaming side (Apache Beam, Flink, & Spark). What is causing the break in our architecture patterns? A huge reason for the break in our existing architecture patterns is the concept of Bound vs. Unbound data. This concept is as fundamental as the Data Lake or Data Hub and we have been dealing with it long before Hadoop. Let’s break down both Bound and Unbound data. Bound Data Bound data is finite and unchanging data, where everything is known about the set of data. Typically Bound data has a known ending point and is relatively fixed. An easy example is what was last year’s sales numbers for Telsa Model S. Since we are looking into the past we have a perfect timebox with a fixed number of results (number of sales). Traditionally we have analyzed data as Bound data sets looking back into the past. Using historic data sets to look for patterns or correlation that can be studied to improve future results. The timeline on these future results were measured in months or years. For example, testing a marketing campaign for the Telsa Model S would take place over a quarter. At the end of the quarter sales and marketing metrics are measured deeming a success or failure for the campaign. Tweaks for the campaign are implemented for next quarter and the waiting cycle continues. Why not tweak and measure the campaign from the first onset? Our architectures and systems were built to handle data in this fashion because we didn’t have the ability to analyze data in real-time. Now with the lower cost for CPU and explosion in Open Source Software for analyzing data, future results can be measured in days, hours, minutes, and seconds. Unbound Data Unbound data is unpredictable, infinite, and not always sequential. The data creation is a never ending cycle, similar to Bill Murray in Ground Hog Day. It just keeps going and going. For example, data generated on a Web Scale Enterprise Network is Unbound. The network traffic messages and logs are constantly being generated, external traffic can scale-up generating more messages, remote systems with latency could report non-sequential logs, and etc. Trying to analyze all this data as Bound data is asking for pain and failure (trust me I’ve been down this road). Our world is built on processing unbound data. Think of ourselves as machines and our brains as the processing engine. Yesterday I was walking across a parking lot with my 5 year old daughter. How much Unbound data (stimuli) did I process and analyze? Watching for cars in the parking lot and calculating where and when to walk Ensuring I was holding my daughter’s hand and that she was still in step with me Knowing the location of my car and path to get to car Puddles, pot holes, and pedestrians to navigate Did all this data (stimuli) come in concise and finite fashion for me to analyze? Of course not! All the data points were unpredictable and infinite. At any time during our walk to the car more stimuli could[...]



Navigating Disruption

Tue, 01 Aug 2017 21:59:59 PDT

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Quantum leaps in navigation are bringing our world closer. Join Walter and guests Brad Parkinson, Stan Honey, Randy Hoffman, Corinne Vigreux and Di-Ann Eisnor as they explore how we got here, and where we’re going next.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6387633/dcd4a420.mp3?CID=311880

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Hello Alice! Scale your business... with AI

Wed, 19 Jul 2017 04:59:59 PDT

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Transforming your business into a digital business is hard. It requires transforming the company's culture, business processes and, most certainly, the IT organization.

Michele Perras, who leads Strategy and Market Development at Pivotal Labs, understands that and explains that often businesses, even those who never considered themselves technology or software-driven companies, have to learn these skills to stay relevant and move forward on their digital transformation journey.

An entrepreneur and founder of an accelerator herself, Perras shares the key elements of helping companies via a fully immersive, co-location based software development consulting program. Businesses take home replicable best practices for business processes, organizational structures and best tools to use, as well as a new mindset and culture needed to improve efficiency, agility and ability to innovate.

In a groundbreaking project, Pivotal, Circular Board and Dell Technologies partnered to develop an artificial intelligence business platform, Alice. Alice, pushing new frontiers in human machine partnerships, connects female founders in real time with mentors, resources and events that help scale their businesses, constantly improving via machine learning.

ENCLOSURE:http://tracking.feedpress.it/link/16994/6392658/6cb86805.mp3?CID=311880

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Disruption: Cha-ching!

Tue, 18 Jul 2017 21:29:59 PDT

The way we value and move money is changing. Again. Join Walter as he explores Bitcoin and the long evolution of currency with guests Neha Narula, Don Tapscott, Felix Martin and Max Levchin. For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts. ENCLOSURE:http://tracking.feedpress.it/link/16581/6383777/756427c2.mp3?CID=311880 [...]



Music: The Sound of Disruption

Tue, 04 Jul 2017 21:29:59 PDT

The way we consume music is continuously changing. Join Walter as he discusses how the industry’s endured unfathomable disruption with guests Ani DiFranco, Amanda Palmer, Robert Harris, Jonathan Taplin, Jim Rondinelli and Bob Lefsetz. For more on these stories go to delltechnologies.com/trailblazers and check out the music Walter's currently listening to on Spotify. Please let us know what you think of the show by leaving us a rating or review in Apple Podcasts. ENCLOSURE:http://tracking.feedpress.it/link/16581/6383778/475b47f0.mp3?CID=311880 [...]



Of Risk and Trust... In IT

Sun, 25 Jun 2017 09:08:59 PDT

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Ray O'Farrell, Chief Technology Officer at VMware, calls virtualization one of the most non-disruptive disruptive technologies to hit the enterprise. Virtualization, or software-defined hardware, has allowed IT organizations to shift into a new role, the role of driving digital transformation by enabling agility and speed for the entire company. This is a big deal because the IT organization's top goal of 'saving money' has evolved into one of actively creating business value. Of course it's not that simple. This new mindset of business value creation has to be cultivated by the leadership and must be accompanied by a cultural shift by the entire IT organization and across the entire company and, ultimately, across the industry. Listen to Ray's practical and visionary insights that will help your business's top line results.

ENCLOSURE:http://tracking.feedpress.it/link/16994/6392659/20d541a2.mp3?CID=311880

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Distributed Analytics Meets Distributed Data with a World Wide Herd

Fri, 23 Jun 2017 06:51:31 PDT

Originally posted on CIO.com by Patricia Florissi, Ph.D. What is a World Wide Herd (WWH)? What does it mean to have “Distributed analytics meet distributed data?” In short, it means having a group of industry experts, in this case a group given the title of World Wide Herd, to form a global virtual computing cluster. The WWH concept creates a global network of distributed Apache Hadoop® instances to form a single virtual computing cluster that brings analytics capabilities to the data. In a recent CIO.com blog, Patricia Florissi, Ph.D., vice president and global CTO for sales and a distinguished engineer for Dell EMC, details how this approach enables analysis of geographically dispersed data, without requiring the data to be moved to a single location before analysis. To illustrate the power of the concept of distributed, yet collaborative, analytics in-place at worldwide scale, it sometimes helps to begin with an example. In this case, I will start with an example from the healthcare industry, and then dive down into discussion of the World Wide Herd, a global virtual computing cluster. Hospitals around the world are moving to value-based healthcare and achieving dramatic reductions in costs. One way to achieve these goals is to make more effective and efficient use of expensive medical diagnostic equipment, such as CT scanners and MRI machines. When a hospital maximizes its utilization of these devices, it increases its ROI and potentially reduces its costs by avoiding the need to buy additional devices. In principle, it is contributing to more affordable care. With a focus on value-based healthcare, Siemens Healthineers, the healthcare business of Siemens AG, is developing a global benchmarking analytics program that will allow its customers to see and compare their device utilization metrics against those of hospitals around the world. The goal is to help hospitals identify opportunities to gain greater value from their investments. This global benchmarking analytics program will be offered via the Siemens Healthineers Digital Ecosystem, a digital platform for healthcare providers, as well as for providers of solutions and services, aimed at covering the entire spectrum of healthcare. The platform, announced in February 2017, will foster the growth of a digital ecosystem linking healthcare providers and solution providers with one another, as well as bringing together their data, applications and services. Global benchmarking analytics in the Siemens Healthineers Digital Ecosystem will be powered by the innovative Dell EMC World Wide Herd technologies, enabling the Internet of Medical Things (IoMT) for several healthcare modalities. Dell EMC’s collaboration with Siemens delivers the ability to analyze data at the edge, where only the analytics logic itself and aggregated intermediate results traverse geographic boundaries to facilitate data analysis across multi-cloud environments—without violating privacy and other governance, risk and compliance constraints. How it works The WWH concept, which was pioneered by Dell EMC, creates a global network of Apache Hadoop® instances that function as a single virtual comp[...]



Advertising: Disrupting Interruption

Tue, 20 Jun 2017 21:29:59 PDT

The story of advertising starts, ends and endures with disruption at its core. Join Walter Isaacson as he discusses digital marketing's past, present and exciting future with industry visionaries Seth Godin and Dave Droga. For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in iTunes. ENCLOSURE:http://tracking.feedpress.it/link/16581/6383779/a92c0e21.mp3?CID=311880 [...]



Dell EMC Takes #1 Position on TPCx-BigBench for Scale Factor 10000

Fri, 26 May 2017 05:00:05 PDT

Dell EMC is focused on providing information that helps customers make the most of their big data technology investment. The failure rate for Hadoop big data projects is still too high given the maturity of the technology.  Customers can’t afford to guess when designing and sizing a solution; they need to deliver optimal performance for their business use cases and to scale as needed. Dell EMC recently completed and published a new TPCx-BigBench (TPCx-BB) result that will help customers make the right choices for Hadoop performance and scalability. Today we are happy to announce that The Dell EMC Ready Bundle for Cloudera Hadoop and Dell EMC PowerEdge R730XD provides the #1 price/performance in TPCx-BigBench for Scale Factor 10000. (*Based on published results as of May 13, 2017) Dell EMC is the industry leading supplier of hyper-converged, converged and “Ready” Solutions by many standards.  Dell EMC’s tested and validated Ready Bundle for Cloudera Hadoop, together with the right performance benchmark results, takes the guess work out of Hadoop implementations. The Transaction Processing Council (TPC) is a non-profit corporation founded to define transaction processing and database benchmarks and to disseminate objective, verifiable TPC performance data to the industry. Benchmarking has long been a standard practice of the computer industry and is used to discover, measure and assess the relative performance of alternative systems and configurations. This information can then be customized or extrapolated as an input to system design for systems that will provide similar services for real world applications. Similar to the years of development and maturation of Relational Database Management Systems (RDBMSs), there is a rapidly expanding ecosystem of both complimentary and competing Big Data Analytics Systems (BDAS). As the big data analytics ecosystem matures, the pressure to evaluate and compare performance and price performance of these systems becomes more useful. To address this need in the industry, the TPC has developed a big data benchmarking specification called TPCx-BB.  The TPCx-BB Benchmark was developed to cover essential functional and business aspects of big data use cases. The benchmark allows for an objective measurement of a BDAS and provides verifiable performance, price/performance, and availability metrics for those considering new investments. Dell is the first to cross the significant milestone of publishing a result using SF10000 which is the largest data set executed thus far for TPCx-BB. SF10000 maps to a roughly 10TB data set which typically takes longer to execute than the smaller Scale Factors (1000 & 3000). How realistic is the benchmark? The TPCx-BB benchmark is designed to stress the CPU and IO systems of a BDAS using one or more concurrent streams.  The test includes 30 unique queries in a simulated workload that is typical of real-world analytic applications. For a test to run and successfully pass an audit, 2 sequential performance runs must be executed. Each run is performed under 3 phases: Load, Power and Throughp[...]



Dell EMC extends its portfolio for Splunk to VxRack FLEX

Thu, 25 May 2017 12:00:56 PDT

Operational Intelligence and machine generated data have been very hot topics lately as organizations are beginning to realize how valuable this data is for the business. For the last few years, Splunk has been the leader in this space with their all-encompassing platform that enables the ability to collect, search and analyze machine generated data. (Not up to speed on this yet? Check out my other blog on getting started with machine generated data) Dell EMC and Splunk have had a tremendous partnership over the past couple years that is based on the premise that we offer market leading infrastructure that is optimal for Splunk’s world class analytics platform for machine generated data. A couple weeks ago, we took this one step further… I’m excited to announce the release of the Solution Guide for Machine Analytics with Splunk Enterprise on VxRack Flex 1000! With this, Dell EMC now has a validated rack scale, hyper-converged infrastructure solution for Splunk that has been jointly validated by Splunk & Dell EMC. Why is this important? Having this solution that has been jointly validated by both Splunk and Dell EMC to “meet or exceed Splunk’s performance benchmarks” gives users a higher degree of confidence in the environment. With this solution the performance needed to run Splunk effectively and gain the valuable insights to make critical IT and business decisions will be there. Our solutions engineering team along with Splunk put hundreds of engineering hours into designing specific configurations based on a variety of different deployment scenarios and rigorously tested them to ensure performance. The solutions guide gives you not only those configurations but also implementation guidelines and deployment practices. All of this equals lower risk, quicker time to value and validated for performance…can’t ask for anything better. How is VxRack Optimal for Splunk? VxRack provides flexible, rack scale, hyper-converged infrastructure that allows you to use the hypervisor of your choice or bare metal as well as the ability to start small but scale-out to thousands of nodes. With VxRack you are given the flexibility to optimize your tiering for Splunk by putting Hot and Warm buckets in SSD while using HHD or even Isilon scale-out NAS for your cold bucket needs (Solution guide shows how to use Isilon for cold tiering). You also get to enjoy the benefits of Software Defined Storage and data services that are essential in today’s data center. The best part is that VxRack gives a turnkey experience that is engineered and designed to be ready to run, giving you a quicker time to insight and value. Additionally, with single support and life-cycle management for your infrastructure you lower complexity and reduce risk and costs. All of this equals great performance, economical tiering structure & easy to deploy and manage infrastructure that is validated to run Splunk. The post Dell EMC extends its portfolio for Splunk to VxRack FLEX appeared first on Dell EMC Big Data. [...]



Store Your IT Data... in DNA?

Wed, 24 May 2017 04:59:59 PDT

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What happens when a large scale business in an industry that is transforming at lightning speed has an unsolvable problem? It's a good thing! It means that it's time for close collaboration between the business and the IT provider to tap into cutting edge research and experience to find a solution. The resulting solution and innovation will then benefit all businesses, big or small, interested in the underlying technology or not. Listen how Dell EMC CTO John Roese leads his team to collaborate and innovate for all customers' benefit and, along the way, hear about some fascinating paths to innovation.

Please let us know what you think of the show by leaving us a rating or review in iTunes.

ENCLOSURE:http://tracking.feedpress.it/link/16994/6392660/80f45e5c.mp3?CID=311880

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Destination: Disruption

Tue, 23 May 2017 21:30:59 PDT

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Before hotels, there was home sharing. In the digital age, Airbnb has brought it back. Walter Isaacson takes a historical look at the hospitality industry, navigating listeners from horse carriage inns to the personalization of the sharing economy.

For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in iTunes.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6383780/448e5ebb.mp3?CID=311880

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Breaking News: Disruption

Tue, 09 May 2017 21:59:59 PDT

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Over 150 years ago, new technology threatened newspapers to the brink of extinction. Sound familiar? In this episode of Trailblazers, Walter Isaacson follows the history of the newspaper industry and explores how it has survived by adopting — not resisting — disruptive innovations. For more on these stories go to delltechnologies.com/trailblazers. Please let us know what you think of the show by leaving us a rating or review in iTunes.

ENCLOSURE:http://tracking.feedpress.it/link/16581/6383781/d33d2f27.mp3?CID=311880

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Jackets lined with data

Wed, 03 May 2017 01:18:25 PDT

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Not only is Columbia Sportswear tough tested, it’s also a story of how technology redefines an industry. Dell EMC helped Columbia be first to market with innovations developed in their 3D lab made possible by the cloud.
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Smarter flights and lights

Wed, 03 May 2017 01:18:25 PDT

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GE is changing everyone's thinking by connecting the physical and digital worlds. Sensor-enabled jet engines and smarter streetlights are just a start with Dell Technologies as a partner.
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