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C2090-102 - IBM Big Data Architect - Dump Information

Vendor : IBM
Exam Code : C2090-102
Exam Name : IBM Big Data Architect
Questions and Answers : 110 Q & A
Updated On : April 17, 2019
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C2090-102 Questions and Answers

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C2090-102 IBM Big Data Architect

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C2090-102 exam Dumps Source : IBM Big Data Architect

Test Code : C2090-102
Test Name : IBM Big Data Architect
Vendor Name : IBM
Q&A : 110 Real Questions

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IBM IBM Big Data Architect

Hortonworks, IBM, crimson Hat Collaborate to help accelerate Containerized huge records Workloads for Hybrid Architectures | killexams.com Real Questions and Pass4sure dumps

Hortonworks records Platform, Hortonworks DataFlow, Hortonworks DataPlane and IBM Cloud inner most for records convey comprehensive enterprise records platforms to crimson Hat OpenShift

ny, Sept. 10, 2018 /PRNewswire/ -- Strata statistics conference - Hortonworks (HDP), IBM (IBM) and RedHat (RHT) these days introduced an Open Hybrid architecture Initiative, a new collaborative effort the businesses can use to build a typical enterprise deployment model it is designed to allow big statistics workloads to run in a hybrid method throughout on-premises, multi-cloud and edge architectures.

Hortonworks emblem. (PRNewsFoto/Hortonworks)

more

as the initial part of the initiative, the agencies plan to work together to optimize Hortonworks statistics Platform, Hortonworks DataFlow, Hortonworks DataPlane and IBM Cloud private for facts for use on RedHat OpenShift, an trade-leading business container and Kubernetes utility platform. this can enable users to develop and deploy containerized large records workloads, in the end making it simpler for consumers to control statistics applications throughout hybrid cloud deployments. additionally, IBM and Hortonworks will lengthen their joint work to integrate key services offered via Hortonworks DataPlane with IBM Cloud private for information.

companies are present process huge company mannequin transformations, powered by the means to technique and analyze new varieties and large amounts of information. in consequence, many are moving to hybrid cloud environments that leverage lightweight microservices in the superior manner possible.

Hortonworks and IBM in the past introduced a collaboration to support agencies accelerate statistics-driven decision-making. present day news builds upon that basis with the intent to carry big information workloads to a modern and container-based mostly foundation, enabling purchasers to deploy Hortonworks and IBM platforms right into a hybrid cloud ambiance powered with the aid of red Hat OpenShift. The initiative contains the following:

  • Hortonworks plans to certify Hortonworks records Platform, Hortonworks DataFlow and Hortonworks DataPlane as purple Hat licensed Containers on red Hat OpenShift and appears ahead to reaching "Primed" designation. moreover, Hortonworks will boost HDP to adopt a cloud-native architecture for on-premises deployments by keeping apart compute and storage and containerizing all Hortonworks records Platform and Hortonworks DataFlow workloads. This allows purchasers to more conveniently undertake a hybrid structure for huge facts functions and analytics, all with the commonplace and relied on safety aspects, information governance and operations that businesses require.
  • IBM, which announced plans past this 12 months to prolong IBM Cloud inner most platform and middleware to the OpenShift platform, has begun the purple Hat OpenShift certification technique for IBM Cloud private for records, and has achieved "Primed" designation because the first part. The move will aid supply the monstrous OpenShift group of developers and clients – which consist of IBM and Hortonworks valued clientele – fast access to strong analytics, statistics science, desktop learning, data administration and governance capabilities, entirely supported throughout hybrid clouds.
  • "Kubernetes is the de facto container orchestration device and we now have been working during this ecosystem since the venture's infancy to assist make it able for all, and particularly the commercial enterprise in red Hat OpenShift. these days we're overjoyed that Hortonworks and IBM Cloud deepest's information portfolio have chosen pink Hat OpenShift because the relied on Kubernetes platform for massive statistics workloads," noted Ashesh Badani, vice president and generic manager, Cloud systems, crimson Hat. "via constructing and managing their functions by means of containers and Kubernetes with OpenShift, consumers and the massive statistics ecosystem have alternatives to carry this subsequent technology of big records workloads to the hybrid cloud and deliver the merits of an agile, efficient, professional, multi-cloud infrastructure."

    "The work that purple Hat, IBM and Hortonworks are doing to modernize enterprise massive information workloads via containerization is geared toward assisting shoppers to take abilities of the agility, economics and scale of a hybrid statistics structure," noted Rob Bearden, chief executive officer of Hortonworks. "The improvements as a result of this collaboration can enable the seamless and trusted hybrid deployment model mandatory today by way of enterprises which are undergoing tremendous company model transformation."

    moreover aggressive and records challenges, companies are also scrambling to deliver purposes once designed for public cloud behind the firewall for improved control, lessen prices, more advantageous safety and more convenient administration. really, in a contemporary IDC Cloud and AI Adoption Survey[1], more than eighty p.c of respondents mentioned they plan to flow or repatriate facts and workloads from public cloud environments in the back of the firewall to hosted deepest clouds or on-premises locations over the next 12 months, since the initial expectations of a single public cloud issuer were not realized.

    Story continues

    "As these dynamics proceed, they may work to sluggish innovation and hinder companies' progression to commercial enterprise AI," mentioned Rob Thomas, prevalent manager, IBM Analytics. "Scaling the ladder to AI calls for robust facts prep, analytics, records science and governance, all of that are with no trouble scaled and streamlined within the variety of containerized, Kubernetes-orchestrated environments that we're speakme about nowadays."

    About Hortonworks Hortonworks is a number one issuer of enterprise-grade, global information administration systems, capabilities and options that convey actionable intelligence from any type of statistics for over half of the Fortune one hundred.  Hortonworks is dedicated to driving innovation in open source communities, providing pleasing cost to business clients. together with its partners, Hortonworks gives technology, competencies and help in order that enterprise customers can undertake a latest data structure.

    Hortonworks and HDP are registered emblems or emblems of Hortonworks, Inc. and its subsidiaries within the united states and different jurisdictions. All other emblems are the property of their respective owners. For greater assistance, please visit www.hortonworks.com.

    Hortonworks' forward-searching Statements This press unlock may comprise "ahead-searching statements" inside the which means of part 27A of the Securities Act of 1933, as amended, and area 21E of the Securities trade Act of 1934, as amended.

    forward-looking statements provide latest expectations of future hobbies in line with definite assumptions and encompass any commentary that doesn't at once relate to any historical or current fact.  They might also consist of (with out obstacle) suggestions regarding our expectations, dreams or intentions involving our offerings, expertise, partnerships, actions, future performance, our adoption of or involvement with others' choices, and others' adoption of or involvement with our offerings.  forward-searching statements are field to universal and unknown risks and uncertainties and are based on probably inaccurate assumptions that may cause genuine results to vary materially from these expected or implied with the aid of the ahead-looking statements. If this kind of risks or uncertainties materialize or if any of the assumptions show wrong, our results could differ materially from the outcomes expressed or implied by way of the forward-looking statements we make. assistance on elements that might affect our fiscal effects and the forward-searching statements during this press unlock are included in our form 10-ok filed on March 15, 2018, our Quarterly report on form 10-Q for the quarter ended June 30, 2018 filed on August 9, 2018, or in different filings we make with the Securities exchange commission occasionally, exceptionally below the caption possibility elements. All forward-searching statements in this press liberate are made as of the date hereof, in keeping with guidance obtainable to us as of the date hereof, and we undertake no responsibility, and don't intend, to update these forward-searching statements. 

    About IBM  For greater about IBM Analytics talk over with www.ibm.com/analytics.

    About red Hat, Inc. crimson Hat is the area's leading company of open source software options, the usage of a neighborhood-powered approach to provide respectable and excessive-performing cloud, Linux, middleware, storage and virtualization technologies. purple Hat also offers award-successful assist, practising, and consulting features. As a connective hub in a world network of businesses, companions, and open supply communities, purple Hat helps create imperative, innovative technologies that liberate components for growth and put together customers for the future of IT. be trained greater at http://www.redhat.com.

    purple Hat's ahead-looking Statements definite statements contained during this press unlock may also represent "forward-searching statements" within the that means of the inner most Securities Litigation Reform Act of 1995. ahead-looking statements supply existing expectations of future routine based on certain assumptions and include any commentary that doesn't at once relate to any old or existing reality. genuine consequences may also vary materially from those indicated via such ahead-looking statements because of various essential elements, together with: dangers related to the ability of crimson Hat to compete readily; the capability to carry and stimulate demand for brand new products and technological improvements on a timely groundwork; delays or discounts in assistance know-how spending; the mixing of acquisitions and the ability to market successfully bought applied sciences and products; risks regarding errors or defects in pink Hat offerings and third-party items upon which crimson Hat choices depend; risks concerning the safety of purple Hat offerings and different statistics security vulnerabilities; fluctuations in alternate charges; the consequences of industry consolidation; uncertainty and adverse outcomes in litigation and related settlements; the inability to effectively protect red Hat intellectual property and the advantage for infringement or breach of license claims of or regarding third celebration intellectual property; adjustments in and a dependence on key personnel; the ability to satisfy financial and operational challenges encountered in our overseas operations; and ineffective administration of, and manage over, crimson Hat's increase and foreign operations, as well as other elements contained in red Hat's most fresh Quarterly file on kind 10-Q (copies of which may be accessed throughout the Securities and change commission's website at http://www.sec.gov), including those found therein beneath the captions "chance elements" and "management's discussion and evaluation of financial condition and outcomes of Operations". apart from these factors, actual future performance, consequences, and effects can also range materially as a result of extra familiar elements including (without difficulty) frequent industry and market conditions and growth fees, financial and political situations, governmental and public coverage alterations and the impact of herbal failures comparable to earthquakes and floods. The ahead-searching statements included in this press liberate characterize crimson Hat's views as of the date of this press unlock and these views may exchange. youngsters, whereas red Hat may select to update these ahead-looking statements at some aspect sooner or later, crimson Hat notably disclaims any responsibility to achieve this. These forward-looking statements should still not be relied upon as representing purple Hat's views as of any date subsequent to the date of this press unencumber.

    purple Hat and OpenShift are emblems or registered trademarks of purple Hat, Inc. or its subsidiaries in the U.S. and other countries.

    Media contact:

    Michelle Lazzar, Hortonworkscomms@hortonworks.com(408) 884-9861

    [1] IDC, Cloud Repatriation hastens in a Multicloud World, doc #US44185818, August 2018

     

    View customary content material to download multimedia:http://www.prnewswire.com/information-releases/hortonworks-ibm-pink-hat-collaborate-to-assist-speed up-containerized-huge-information-workloads-for-hybrid-architectures-300709451.html


    An architecture developed for top-efficiency Healthcare | killexams.com Real Questions and Pass4sure dumps

    “The healthcare industry is exploding with new tips, and as we proceed to stream against superior medical analytics, we comprehend this vogue isn't slowing down,” states Bryan Fiekers, Senior Director of research at HIMSS Analytics, in a examine that focus on modernizing healthcare know-how.

    This remark practically sums up one of the most greatest challenges facing healthcare groups today. And it additionally highlights probably the most strongest opportunities for those that want to be at the forefront of transforming biomedical research and advancing precision drugs.

    in the literally exabytes of statistics flooding into scientific research and repair beginning agencies, nowadays lie insights that can store lives, cause new discoveries, and radically change affected person care – for the organizations inclined to optimize the style they leverage, analyze, and make the most of their most effective aid: facts.

    here's the message that IBM methods will carry to the 2019 HIMSS Annual conference and Exhibition (HIMSS19) in Orlando, Florida, on February eleven-15. at the back of this message exists a proven answer that helps one of the most greatest leading precision medication initiatives around the globe – the IBM high-efficiency information and AI (HPDA) reference structure for healthcare– a foundation that can support organizations leverage the alternatives inherent in their huge new records sets.

    whether you're a health insurer that should supply pleasant features to help people make enhanced decisions to keep their health; a college researcher publishing journal articles looking to win your next circular of grant funding; a data scientist in industrial R&D company progressing competencies drugs via scientific trials; or a physician in a health facility providing cures so as to supply patients the highest quality scientific effects – with HPDA you are going to experience new information for pace and scale. in the 21st century, healthcare stakeholders want a legit, flexible, and secure computing platform that meets their distinct utility requirements.

    via our work with many overseas groups and healthcare industry enterprise partners, IBM has considered that groups that adopted our HPDA reference structure for genomics, imaging, EHR, deep discovering and other large facts initiatives in biomedical analysis have completed valuable consequences and demanding benefits.

    At HIMSS19, IBM will characteristic a number of precise-world examples of HPDA architectures and add-ons assisting agencies seriously change their capabilities to benefit perception from huge datasets.

    for instance, Aetna improves personalised care with the aid of providing precise-time entry to guidance and quality functions that help tens of thousands and thousands of americans make better selections to keep their fitness and manipulate prices. The session at the IBM booth #6459 at HIMSS19 will show how the inclusion of high-efficiency IBM storage and utility-described infrastructure superior the standard efficiency and velocity to birth of the commercial enterprise data warehouse, allowing Aetna to research large quantities of information quicker, while addressing compliance and privacy-connected concerns – and slicing expenses.

    booth guests will also learn how Thomas Jefferson college is coupling giant collections of diverse organic datasets with high-efficiency computing to force new advances in precision medication. The Jefferson team has generated evidence from processing large quantities of statistics to reveal that an individual’s sex, race/ethnicity, and geographic starting place affect predisposition for ailment, how a disease might growth, and the therapeutic options that could be most crucial to pursue.

    The IBM HPDA architecture is developed on IBM’s history of offering highest quality practices in high-efficiency computing (HPC). in fact, the primary HPDA framework become used to construct Summit and Sierra – presently two of the world’s strongest supercomputers designed for records and AI. The structure is designed to support healthcare and lifestyles science organizations with ease scale compute and storage resources as demand grows, and to help the wide range of construction frameworks and functions required for industry innovation – all without useless re-investments in technology.

    Our reference structure for healthcare contains key infrastructure accessories from IBM’s excessive-efficiency compute and storage portfolio, and it supports an increasing ecosystem of leading industry companions. The IBM HPDA architecture is in accordance with IBM Storage and IBM utility-described Storage solutions, software-defined infrastructure, IBM POWER9 servers, and the IBM POWERAI complete AI platform. It defines a highly bendy, scalable, and budget friendly platform for having access to, managing, storing, sharing, integrating, and analyzing large statistics inside the constraints of constrained IT budgets. IT companies can use the reference structure as a excessive-degree book for overcoming data administration challenges and processing bottlenecks generally encountered in personalized healthcare initiatives and different compute- and records-intensive biomedical workloads.

    Introducing the brand new capabilities of the IBM HPDA structure and proposing consumer success reports aren’t the best highlights IBM techniques will characteristic at HIMSS19. we are able to also host a breakfast briefing panel dialogue protecting the important role that an HPDA architecture can play in helping healthcare companies solve probably the most largest analysis and analytics challenges they face. during this panel, Aetna, representatives from Louisiana State college, and Froedtert health will share how they're accelerating discoveries and enhancing personalised care through the use of flexible, scalable, secure, and economical excessive-efficiency systems that may leverage the cloud to make certain the highest ranges of facts availability, reliability, and compliance.

    Come meet our IBM techniques HPDA business experts for a demo on the IBM booth #6459. And earlier than you go, get a glimpse of our full periods time table in this weblog – Making precision medicine a fact.


    massive records becomes architecture in this CNC-Milled reveal Wall for IBM | killexams.com Real Questions and Pass4sure dumps

    large data turns into architecture in this CNC-Milled display Wall for IBM

    Big Data Becomes Architecture in This CNC-Milled Screen Wall for IBM, Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure

    Responding partly to recent debates on how large information will affect our developed environments, Synthesis Design + structure have teamed up with IBM Watson Analytics to design an indoors characteristic wall for the Watson event middle in San Francisco. The assignment, named facts Moiré after the dizzying patterns created with the aid of overlapping units of lines, uses records from the affect of mobile phones on month-to-month consumer spending to create a specific display material that defines the wall.

    Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + Architecture + 31

    Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + architecture

    The information-pushed patterns on the floor of the wall are end result of the parametric modeling in accordance with Watson's personal facts evaluation, hence no longer most effective forming part of the showroom but actively displaying off the capabilities of the system itself. The generative design turned into CNC milled onto two sheets of aluminum, which collectively create an complicated monitor of counsel that's lit from between the two layers.

    Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure

    by itself, the installation is a standout commercial for IBM's showroom. with none rationalization, the design offers little indication that it represents a massive facts set. but within the presence of a knowledgeable e book, the complete narrative of the house as the materialization of an abstract set of records gives new meaning. "whereas the project is driven via information, the legibility of that records is summary rather than literal," explains Synthesis Design + architecture in a press release. "rather than supply a statistical or analytical reading of the records, the project as an alternative gives a spatial and atmospheric reading that enhances the journey of company to the core and encourages them to engage spatially."

    Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure

    data Moiré's gentle spiral form permits company to develop into immersed in a tangible component that, due to the baffling scale of the facts it represents, could be unimaginable to draw close in any other case. That it required a supercomputer to make satisfactory feel of the data for it to materialize within the variety of aluminum monitors opens a complete dialogue in regards to the role of the human in the sea of information we create day by day. 

    © Arktura LLC © Arktura LLC Courtesy of Synthesis Design + Architecture Courtesy of Synthesis Design + structure

    Architects: Synthesis Design + ArchitectureLocation: San Francisco, USAClient: IBM WatsonStatus: achieved 2016Program​: characteristic WallArea: 35 sfDesign crew: Alvin Huang (primary), Mo Harmon, Farnoosh RafaieCollaborators: IBM Watson Analytics (records evaluation), Arktura LLC - los angeles (fabrication), Gensler - San Francisco (interiors)

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    IBM Big Data Architect

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    Big Data analysis or data acquisition? | killexams.com real questions and Pass4sure dumps

    Centralized data architectures are adapting to new opportunities for data collection and analytics.

    The starting point of a discussion with data and advanced analytics needs to begin with Moore’s Law. In 1965, Gordon Moore, a co-founder at Intel, noticed the number of transistors on a chip doubled every year while the price was cut in half. He predicted this trend would continue. And while the number of transistors per chip has slowed recently, researchers find the basic point is still true.

    Data storage prices have plummeted, prices have crashed, and computers are a fraction of the size they used to be. And yes, what can be computed on the same-sized and priced chip has increased many times over.

    We read about the results of this market transition every day: the data explosion; pervasive sensing and connectivity; the Internet of Things (IoT) with 50 billion endpoints; and how today’s smartphones are more capable in computing, storage and input/output (I/O) than not only early mainframes, but even Deep Blue (1997/IBM). This even applies to companies such as ExxonMobil, which made a $2 billion investment in Lockheed Martin to drive open systems architecture for process automation. For some companies, the price advantages of Moore’s Law have been too long in coming to manufacturing’s front lines.

    The result of this ubiquitous and inexpensive computing is process manufacturers have an opportunity to re-imagine their data analytics strategies by implementing options that used to be too expensive to consider. Data used to be centralized for analytics because it was expensive to collect, store, and analyze.

    Ubiquitous and less expensive computing will continue. Regardless of whatever one calls it–Industrie 4.0, smart manufacturing, digital transformation–the question is what to do with the data being collected.

    New words for new models

    New economics and ubiquitous computing mean the Purdue-model centralized approach (Figure 1) is being adapted to new opportunities. The centralized model is the most common architecture in process plants worldwide and is familiar to users.

    Figure 1: An explosion of new architectures for data collection, storage, and analysis is modifying the Purdue Model. Courtesy: Seeq

    Figure 1: An explosion of new architectures for data collection, storage, and analysis is modifying the Purdue Model. Courtesy: Seeq

    The Purdue model is being updated to take advantage of new technologies such as:

  • Wireless systems integrating new sensors into existing control and monitoring deployments, either within a plant or at distance, to expand operational visibility.
  • Edge computing, which is a broad term that includes local data storage, analytics, and actions.
  • Cloud computing, which is simply renting computing, storage, and analytics from a vendor. This enables two key scenarios:
  • First, a “direct to cloud” approach for data collection, storage, and analytics for sensor telemetry. This typically is referred to as an Industrial Internet of Things (IIoT) use case where data goes directly from end points to cloud storage.
  • Second, for data already collected to be further aggregated for comparison across plants, by using an enterprise historian, or combined with other manufacturing and business data sets to enable broader analytics, often referred to as a data lake.
  • These approaches are not exclusive, and most companies will use more than one, if not all. For example, a plant could bring in data from newly deployed wireless sensors to augment existing plant analytics. This could be combined with data from suppliers, data from raw materials transportation such as temperature and humidity, and data from quality instruments for richer analytics and insights.

    Further, options will expand as new solutions are announced. Into what category should one put the Amazon AWS and Microsoft Azure on-premise products, Amazon Outposts (Figure 2) and Azure Stack, which put their cloud software platforms on server hardware intended for local hosting in an end user’s IT department? Perhaps it’s public cloud, private cloud, and local cloud?

    Figure 2: AWS Outposts and similar services put a vendor’s cloud software platform on server hardware running locally in a customer’s IT department, creating a local cloud. Courtesy: Seeq

    Figure 2: AWS Outposts and similar services put a vendor’s cloud software platform on server hardware running locally in a customer’s IT department, creating a local cloud. Courtesy: Seeq

    With a local cloud IoT scenario, data could be routed from new sensors directly into a company’s IT department server room to ensure strict data governance and to address security issues.

    Some of these options will sound familiar to industry insiders. For example, vendors focused on edge computing will be hard pressed to explain how edge computing components differ from real-time units (RTUs). Similarly, cloud vendors pushing data lakes may be hard pressed to explain why their approach is substantially different than an enterprise historian that rolls up individual plant data.

    It may be hard to separate out the vendor self-interest at play. Thus, it’s not a coincidence a fully decentralized and networked architecture of computing endpoints is being championed by the vendors who sell the required networking, CPUs, and operating systems.

    Planning for ubiquity

    Technology offerings clearly are evolving faster than the language used to explain them. Entire plants sometimes are considered the new edge. The other issue is the mismatch of innovation and time to implement. Products and marketing can be invented overnight. This is far faster than proof points and best practices from successful deployments.

    Flexibility is being driven by lower costs and improved connectivity for where and how to deploy sensors, data collection, storage, and analytics. A comprehensive view of possible architectures and tradeoffs would be well beyond the scope of any article because by the time someone published a compendium a new crop of innovations and buzzwords would have sprung up. With these limitations in mind, here are four considerations about ubiquitous computing’s impact on plant architectures for data creation, collection, storage, and analytics.

    1. What’s the starting point for a company?

    For new sensor deployments, the collected data resides in the cloud even if the cloud is, in fact, hardware in a local data center. Of course, this is more appropriate for monitoring/visibility as the architecture will have to support communications disruptions in addition to meeting a host of data security requirements. Microsoft, Amazon, Google, and 100+ startups, including industrial-specific wireless companies, offer a full stack of “edge to insight” software. This approach offers rapid deployment and new cloud services revenue.

    The alternative is brownfield plants where the center of gravity is, and will continue to be, on premise. Low latency, guaranteed networking, and local access to data are all key to this model, and these solutions are in place and working. A more likely scenario for brownfield plants is expansion of their data collection, either through local wireless solutions or through an adjacent cloud-based system where data is integrated with their plant systems. Where the data lands is flexible with this model, either on premise or in the cloud.

    2. Does a company’s assets have neighbors?

    In the edge computing model, the premise is any asset may be analyzed individually and diagnosed for predictive failure, optimized run time performance, etc. If the asset is operating independently, that makes sense. This, in effect, creates a smarter RTU model for high value assets.

    However, if the asset has neighbors with a process unit or a line of machinery, which is often the case, it’s not so obvious where the data collection and analytics should occur. What can happen is adjacent assets end up fighting for optimization status. What’s needed is optimization of an entire process unit or manufacturing line.

    The answer is aggregating the data from multiple assets at the line, unless it’s part of a larger unit. Careful planning for where data is collected, stored, and analyzed for optimization will be required except for in a truly stand-alone asset scenario.

    Even then—given pricing, energy, and other inputs not available on the plant floor—it may mean the best result is where the system started, with a centralized data collection and analytics model.

    3. Who owns the data?

    With new computing architectures, the question is not only where should data be stored, but by whom. Asset vendors increasingly are offering remote monitoring services for the assets they sell.This leads to several questions regarding data governance such as:

  • Who owns the generated data?
  • Does the data get copied twice (to the monitoring vendor and the plant owner)?
  • How and to where does the asset data move (security, wireless, cloud), etc.?
  • How are the insights brought back and integrated into the customer’s systems for operational improvements?
  • 4. Who has the expertise?

    Data inputs beyond the ones needed for real-time control and monitoring often are required to optimize asset, line, or plant performance. Examples are overtime costs for staff, rush order costs for spare parts, and commitments to customers.

    These factors go into production optimization and are why plant engineers and experts have such encouraging employment prospects. Creating asset, plant, and performance context still is largely a function of employee experience and expertise. This will require advanced analytics offerings to access, visualize, and contextualize data to create insights (Figure 3).

    Figure 3: Seeq is designed for self-service use by process engineers and experts, who are best-positioned to derive insights from data. Courtesy: Seeq

    Figure 3: Seeq is designed for self-service use by process engineers and experts, who are best-positioned to derive insights from data. Courtesy: Seeq

    This means that the employee’s access, whether it’s mobile or web-based, is critical regardless of decisions around edge or cloud data collection. Employees in the plant or headquarters should still centralize the data for analytics. This impacts data governance by dictating who gets to see what data as much as where the data is collected.

    Some rules still apply

    It is easier to start from the innovation and opportunities and work backwards to reality. In reality, innovation planning starts with the security, manageability, and other business priorities, and works backwards from there. Security dominates the discussion; especially as data collection and analytics are distributed. Furthermore, while edge or local devices sound more secure, the increasing sophistication of the systems increases their threat exposure. The more responsibility these components have for equipment operations, the more valuable they are as a potential target.

    Reliability is the second issue after security. Data connectivity and bandwidth will be particularly important. Planning must include a discussion about what happens to the data when the network fails to the edge or the cloud.

    New architecture considerations impacts how and where data is collected and analyzed. Information technology (IT), production, and business results related to return on investment (ROI) will have to be balanced.

    New approaches for data

    Pervasive computing propelled by lower prices for all aspects of computing is driving a new set of approaches for plant data architectures. In effect, this spreads out computing and analytics and prevents everything from being in one spot. Expecting Big Data and machine learning analytics to be applied to broader data sets is a key driver of this belief.

    Innovation is outpacing customers’ adoption of it. Big Data was first, then Industrie 4.0 and the like. Now there are models for new computing innovation at every point in the process. Balancing what is now possible with the requirements for improving performance is the challenge with future innovations.

    Michael Risse, chief marketing officer (CMO) and vice president, Seeq Corp. www.seeq.com Edited by Chris Vavra, production editor, Control Engineering, CFE Media, cvavra@cfemedia.com.

    MORE ANSWERS

    Keywords: Big Data, asset management, cybersecurity

    Even with the advent of Smart Manufacturing and Industrie 4.0, process manufacturers need to know what to do with the data they’re collecting.

    Process manufacturers need to plan for ubiquity and ensure their data is preserved and secure.

    Computing innovations need to be balanced with improved performance.

    Consider this

    What changes will happen 10 or 20 years from now in manufacturing thanks to Big Data?

    Want this article on your website? Click here to sign up for a free account in ContentStream® and make that happen.


    Workload acceleration with the IBM POWER vector-scalar architecture | killexams.com real questions and Pass4sure dumps

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    February 25, 2015

    Michael Gschwind IBM Systems, Poughkeepsie, NY 12601

    architecture development in the IBM Systems division where he is a

    Senior Technical Staff Member and Senior Manager of the Systems

    Architecture team Dr Gschwind defined the vector-scalar

    architecture as the architecture lead for the PERCS (productive,

    easy-to-use, reliable computing system) project defining the future

    POWER7 processor, and continues to lead its development as

    IBM Systems Architecture Chief Architect Prior to his current role,

    he was Floating-Point Chief Architect and Unit Lead, and Technical

    Lead for core reliability for the Blue Gene*/Q In addition to his

    hardware development roles, Dr Gschwind has also held key

    software leadership roles; he developed the first Cell compiler and

    served as technical lead for the Cell software environment Most

    recently, he also served as Chief Architect for the OpenPOWER

    software environment where he led the definition of ABIs and APIs

    Dr Gschwind received his Ph D degree from Technische

    Universität Wien in Vienna, Austria in 1996 He has published

    numerous articles and received over 100 patents in the area of

    computer architecture In 2006, Dr Gschwind was recognized as IT

    Innovator and Influencer by ComputerWeek He is a member of the

    ACM (Association for Computing Machinery) SIGMICRO (Special

    Interest Group on Microarchitecture) Executive Board, a Member of

    the IBM Academy of Technology, an IBM Master Inventor, an

    ACM Distinguished Speaker, and an IEEE (Institute of Electrical

    and Electronics Engineers) Fellow

    14 : 18 M. GSCHWIND IBM J. RES. & DEV. VOL. 60 NO. 2/3 PAPER 14 MARCH/MAY 2016


    Hortonworks, IBM, Red Hat Collaborate to Help Accelerate Containerized Big Data Workloads for Hybrid Architectures | killexams.com real questions and Pass4sure dumps

    Hortonworks Data Platform, Hortonworks DataFlow, Hortonworks DataPlane and IBM Cloud Private for Data bring comprehensive enterprise data platforms to Red Hat OpenShift

    NEW YORK, Sept. 10, 2018 /PRNewswire/ -- Strata Data Conference - Hortonworks (HDP), IBM (IBM) and RedHat (RHT) today announced an Open Hybrid Architecture Initiative, a new collaborative effort the companies can use to build a common enterprise deployment model that is designed to enable big data workloads to run in a hybrid manner across on-premises, multi-cloud and edge architectures.

    Hortonworks logo. (PRNewsFoto/Hortonworks)

    More

    As the initial phase of the initiative, the companies plan to work together to optimize Hortonworks Data Platform, Hortonworks DataFlow, Hortonworks DataPlane and IBM Cloud Private for Data for use on RedHat OpenShift, an industry-leading enterprise container and Kubernetes application platform. This can enable users to develop and deploy containerized big data workloads, ultimately making it easier for customers to manage data applications across hybrid cloud deployments. In addition, IBM and Hortonworks will extend their joint work to integrate key services offered through Hortonworks DataPlane with IBM Cloud Private for Data.

    Enterprises are undergoing massive business model transformations, powered by the ability to process and analyze new types and tremendous amounts of data. As a result, many are moving to hybrid cloud environments that leverage lightweight microservices in the most efficient manner possible.

    Hortonworks and IBM previously announced a collaboration to help businesses accelerate data-driven decision-making. Today's news builds upon that foundation with the intent to bring big data workloads to a modern and container-based foundation, enabling customers to deploy Hortonworks and IBM platforms into a hybrid cloud environment powered by Red Hat OpenShift. The initiative includes the following:

  • Hortonworks plans to certify Hortonworks Data Platform, Hortonworks DataFlow and Hortonworks DataPlane as Red Hat Certified Containers on Red Hat OpenShift and looks forward to achieving "Primed" designation. In addition, Hortonworks will enhance HDP to adopt a cloud-native architecture for on-premises deployments by separating compute and storage and containerizing all Hortonworks Data Platform and Hortonworks DataFlow workloads. This allows customers to more easily adopt a hybrid architecture for big data applications and analytics, all with the common and trusted security features, data governance and operations that enterprises require.
  • IBM, which announced plans earlier this year to extend IBM Cloud Private platform and middleware to the OpenShift platform, has begun the Red Hat OpenShift certification process for IBM Cloud Private for Data, and has achieved "Primed" designation as the first phase. The move will help provide the vast OpenShift community of developers and users – which include IBM and Hortonworks clients – fast access to robust analytics, data science, machine learning, data management and governance capabilities, fully supported across hybrid clouds.
  • "Kubernetes is the de facto container orchestration system and we have been working in this ecosystem since the project's infancy to help make it ready for all, and especially the enterprise in Red Hat OpenShift. Today we are thrilled that Hortonworks and IBM Cloud Private's data portfolio have selected Red Hat OpenShift as the trusted Kubernetes platform for big data workloads," said Ashesh Badani, vice president and general manager, Cloud Platforms, Red Hat. "By building and managing their applications via containers and Kubernetes with OpenShift, customers and the big data ecosystem have opportunities to bring this next generation of big data workloads to the hybrid cloud and deliver the benefits of an agile, efficient, reliable, multi-cloud infrastructure."

    "The work that Red Hat, IBM and Hortonworks are doing to modernize enterprise big data workloads via containerization is aimed at helping customers to take advantage of the agility, economics and scale of a hybrid data architecture," said Rob Bearden, chief executive officer of Hortonworks. "The innovations resulting from this collaboration can enable the seamless and trusted hybrid deployment model needed today by enterprises that are undergoing significant business model transformation."

    In addition to competitive and data challenges, organizations are also scrambling to bring applications once designed for public cloud behind the firewall for greater control, lower costs, greater security and easier management. In fact, in a recent IDC Cloud and AI Adoption Survey[1], more than 80 percent of respondents said they plan to move or repatriate data and workloads from public cloud environments behind the firewall to hosted private clouds or on-premises locations over the next year, because the initial expectations of a single public cloud provider were not realized.

    Story continues

    "As these dynamics continue, they'll work to slow innovation and hinder companies' progression to enterprise AI," said Rob Thomas, general manager, IBM Analytics. "Scaling the ladder to AI demands robust data prep, analytics, data science and governance, all of which are easily scaled and streamlined in the kind of containerized, Kubernetes-orchestrated environments that we're talking about today."

    About Hortonworks Hortonworks is a leading provider of enterprise-grade, global data management platforms, services and solutions that deliver actionable intelligence from any type of data for over half of the Fortune 100.  Hortonworks is committed to driving innovation in open source communities, providing unique value to enterprise customers. Along with its partners, Hortonworks provides technology, expertise and support so that enterprise customers can adopt a modern data architecture.

    Hortonworks and HDP are registered trademarks or trademarks of Hortonworks, Inc. and its subsidiaries in the United States and other jurisdictions. All other trademarks are the property of their respective owners. For more information, please visit www.hortonworks.com.

    Hortonworks' Forward-Looking Statements This press release may contain "forward-looking statements" within the meaning of Section 27A of the Securities Act of 1933, as amended, and Section 21E of the Securities Exchange Act of 1934, as amended.

    Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact.  They may include (without limitation) information regarding our expectations, goals or intentions regarding our offerings, technology, partnerships, activities, future performance, our adoption of or involvement with others' offerings, and others' adoption of or involvement with our offerings.  Forward-looking statements are subject to known and unknown risks and uncertainties and are based on potentially inaccurate assumptions that could cause actual results to differ materially from those expected or implied by the forward-looking statements. If any such risks or uncertainties materialize or if any of the assumptions prove incorrect, our results could differ materially from the results expressed or implied by the forward-looking statements we make. Information on factors that could affect our financial results and the forward-looking statements in this press release are included in our Form 10-K filed on March 15, 2018, our Quarterly Report on Form 10-Q for the quarter ended June 30, 2018 filed on August 9, 2018, or in other filings we make with the Securities Exchange Commission from time to time, particularly under the caption Risk Factors. All forward-looking statements in this press release are made as of the date hereof, based on information available to us as of the date hereof, and we undertake no obligation, and do not intend, to update these forward-looking statements. 

    About IBM  For more about IBM Analytics visit www.ibm.com/analytics.

    About Red Hat, Inc. Red Hat is the world's leading provider of open source software solutions, using a community-powered approach to provide reliable and high-performing cloud, Linux, middleware, storage and virtualization technologies. Red Hat also offers award-winning support, training, and consulting services. As a connective hub in a global network of enterprises, partners, and open source communities, Red Hat helps create relevant, innovative technologies that liberate resources for growth and prepare customers for the future of IT. Learn more at http://www.redhat.com.

    Red Hat's Forward-Looking Statements Certain statements contained in this press release may constitute "forward-looking statements" within the meaning of the Private Securities Litigation Reform Act of 1995. Forward-looking statements provide current expectations of future events based on certain assumptions and include any statement that does not directly relate to any historical or current fact. Actual results may differ materially from those indicated by such forward-looking statements as a result of various important factors, including: risks related to the ability of Red Hat to compete effectively; the ability to deliver and stimulate demand for new products and technological innovations on a timely basis; delays or reductions in information technology spending; the integration of acquisitions and the ability to market successfully acquired technologies and products; risks related to errors or defects in Red Hat offerings and third-party products upon which Red Hat offerings depend; risks related to the security of Red Hat offerings and other data security vulnerabilities; fluctuations in exchange rates; the effects of industry consolidation; uncertainty and adverse results in litigation and related settlements; the inability to adequately protect Red Hat intellectual property and the potential for infringement or breach of license claims of or relating to third party intellectual property; changes in and a dependence on key personnel; the ability to meet financial and operational challenges encountered in our international operations; and ineffective management of, and control over, Red Hat's growth and international operations, as well as other factors contained in Red Hat's most recent Quarterly Report on Form 10-Q (copies of which may be accessed through the Securities and Exchange Commission's website at http://www.sec.gov), including those found therein under the captions "Risk Factors" and "Management's Discussion and Analysis of Financial Condition and Results of Operations". In addition to these factors, actual future performance, outcomes, and results may differ materially because of more general factors including (without limitation) general industry and market conditions and growth rates, economic and political conditions, governmental and public policy changes and the impact of natural disasters such as earthquakes and floods. The forward-looking statements included in this press release represent Red Hat's views as of the date of this press release and these views could change. However, while Red Hat may elect to update these forward-looking statements at some point in the future, Red Hat specifically disclaims any obligation to do so. These forward-looking statements should not be relied upon as representing Red Hat's views as of any date subsequent to the date of this press release.

    Red Hat and OpenShift are trademarks or registered trademarks of Red Hat, Inc. or its subsidiaries in the U.S. and other countries.

    Media contact:

    Michelle Lazzar, Hortonworkscomms@hortonworks.com(408) 884-9861

    [1] IDC, Cloud Repatriation Accelerates in a Multicloud World, doc #US44185818, August 2018

     

    View original content to download multimedia:http://www.prnewswire.com/news-releases/hortonworks-ibm-red-hat-collaborate-to-help-accelerate-containerized-big-data-workloads-for-hybrid-architectures-300709451.html



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