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70-475 Design and Implement gargantuan Data Analytics Solutions

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Test Code : 70-475
Test title : Design and Implement gargantuan Data Analytics Solutions
Vendor title : Microsoft
braindumps : 42 true Questions

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Microsoft Design and Implement Big

Microsoft (70-475): Designing and implementing massive statistics Analytics solutions | true Questions and Pass4sure dumps

MeasureUp’s 70-475: Designing and enforcing huge information Analytics options follow examine is designed to aid candidates prepare for and slither the Microsoft 70-475 examination.

The Microsoft 70-475 examination is putative for statistics experts who've journey designing big data analytics options on Microsoft Azure. Candidates should noiseless exist in a position to design gargantuan records batch processing and interactive solutions in addition to gargantuan records actual-time processing solutions. Candidates may noiseless even exist in a position to design computer-researching options and create and exploit end-to-end cloud analytics solutions.

Certification: This exam counts as credit toward birthright here certifications:specialist

Watch Microsoft's MWC 2019 reside tide reform birthright here | true Questions and Pass4sure dumps

MWC 2019

MWC 2019

Microsoft is making its first gargantuan appearance at mobile World Congress (MWC) in quite ages this yr, and you'll exist in a position to circulation the business's keynote as it happens. Set to kick off on February 24 at 9 a.m. PT (12 p.m. ET / 6 p.m. CET), the keynote will role "new innovations for the future of computing" with comments from Microsoft CEO Satya Nadella and HoloLens chief Alex Kipman.

which you can commemorate along with the keynote are living, tune in with the video embed above or at Microsoft's adventure web page.

As for what we're anticipating Microsoft to announce, every separate signs point to a 2d iteration of the company's HoloLens augmented verisimilitude headset. We're truly not expecting any shock mobile bulletins, however Kipman's involvement strongly tips at a HoloLens 2 exhibit. it truly is far from definite, of route, but every separate they ought to proceed off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.

The orthodox HoloLens has more often than not loved success as an enterprise tool, bolstering manufacturing tactics, along with design and scientific purposes. The headset has even led to a lucrative $480 million shrink with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.

when you account that the orthodox HoloLens launched in 2016, a followup is hotly predicted. it's already been tested that the HoloLens 2 will recreation a custom Holographic Processing Unit (HPU) with an AI coprocessor, enabling it to natively achieve in force deep neural networks to research objects and statistics without sending the repose to the cloud. although, they prognosticate several other advancements, together with, potentially, a change to an ARM processor and aid for LTE.

sooner or later, we'll must wait and spot what Microsoft has in maintain for its MWC 2019 keynote.

This achieve up may embrace affiliate hyperlinks. perceive their disclosure policy for extra particulars.

AMD Navi GPUs could perceive gargantuan performance expand With Variable expense Shading Tech | true Questions and Pass4sure dumps

A these days posted patent software means that AMD can exist infusing a technology called Variable expense Shading (VSR) into its upcoming Navi GPU architecture, which is expected to arrive later this yr. The patent software does not really specify Navi, though it's definitely a probability, given when the patent changed into filed and subsequent date of booklet. AMD filed the patent in August 2017, and it became published the day prior to this. AMD had already been establishing its Navi GPU architecture in 2017, and had additionally begun taping out its 7-nanometer FinFET manufacturing fashion around the identical time (a tape out is when a company enjoy AMD finalizes its semiconductor design before sending it off for manufacturing).

VSR is a rendering approach designed to subside the cross on the GPU and other elements, for introduced efficiency without successful to the perceived visual constancy of a scene. It accomplishes this by means of breaking up a frame into distinctive sections and rendering handiest parts of a frame in complete aspect, those being the enviornment(s) you are most concentrated on, and using coarser pixels across the fringe of a person's imaginative and prescient.

VR headsets stand to the benefit the most from this tech, the status your eyes are concentrated on the core. That talked about, VSR might conceivably exist used in alternative routes for gaming on the desktop. This may especially exist really useful at greater resolutions which are greater taxing on a GPU, equivalent to 4K, and eventually 8K if they want to study that far down the street. AMD is not by myself in seeing the handicap of VSR. NVIDIA carried out a edition of the technology into its Turing GPU architecture. here's NVIDIA's explanation of VSR:

Variable cost Shading is a brand new, simple to achieve into sequel rendering fashion enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by artery of applying varying quantity of processing power to distinctive areas of the picture. VRS works with the aid of various the variety of pixels that may moreover exist processed through a separate pixel shader operation. separate pixel shading operations can now exist applied to a obscure of pixels, permitting applications to effectively vary the shading expense in diverse areas of the monitor. Variable rate Shading can moreover exist used to render extra efficaciously in VR through rendering to a surface that closely approximates the lens corrected picture it is output to the headset monitor. This avoids rendering many pixels that could otherwise exist discarded earlier than the photograph is output to the VR headset.

there is actually widespread activity in VSR. concerning that, Microsoft moreover owns a VSR patent and is working to implement it into the DirectX API. no matter if it extends past VR headsets and into the realm of desktop monitors, notwithstanding, they are able to necessity to wait and spot.

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Design and Implement gargantuan Data Analytics Solutions

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Reporting & Analytics - gargantuan Data Developer# 125072 | true questions and Pass4sure dumps

eFinancialCareers jobs: Reporting & Analytics - gargantuan Data Developer# 125072 in Credit Suisse -, Raleigh, NC, USA
  • Salary: Competitive
  • Location: Raleigh, NC, USA
  • Job Type: Permanent, complete time
  • Company: Credit Suisse -
  • Updated on: 02 Mar 19
  • We OfferCredit Suisse is seeking a Data Engineer in the Enterprise Services -

    SMO - Enterprise Services are responsible for providing innovative ways through maintaining knowledge of industry criterion methodology, technology trends and product availability. Developing trade and technical requirements through consultation with key client trade partners and ensuring consistency to corporate standards for risk and security compliance.

    The Analytics & Reporting team works as portion of a globally distributed team that will exist the primary provider of IT Operational Reports to the firm. At any given time you will confederate with team members across every separate regions and time zones while reporting to a line manager in recent York. You will collaborate with key clients to develop dashboards that will drive trade decisions across the firm.


  • Design/Maintain/Support Bigdata environment, including ETL.
  • Work directly with key trade partners to understand data requirements and deliver on timely manner.
  • Analyze big data sets.
  • Design very tangled SQL statements, including Impala.
  • Scripting automation solutions for Data and Reports.
  • Adhere with Agile methodology and follow the process to maintain/ advocate and to document the details.
  • Ability to easily communicate tangled problem solving ideas to senior management and peers
  • Find opportunities on automation for any manual process.
  • In this function, you will exist designing and implementing data solutions via Bigdata and python. You will moreover build automation scripts to abet minimize manual involvement when pile reports or dashboards. You will moreover abet migrate existing tangled SQL tables into Bigdata, and advocate reporting team to integrate the migrated tables in Tableau.

    You should moreover exist well versed with data design and gain ETL process experience.

    Credit Suisse maintains a Working Flexibility Policy, theme to the terms as set forth in the Credit Suisse United States Employment Handbook.

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  • You gain a degree or higher education in Information Technology or equivalent travail experience
  • You gain at least 3 to 4 years of experience pile Bigdata environment
  • You gain demonstrable experience with Data Engineering
  • You gain deep knowledge in Bigdata impala/Hadoop
  • You gain skills in Python for pile functions using Pyspark and other packages related to Python
  • Database architect for bigdata environment on how the ETL process works will exist an advantage
  • Good interpersonal skills as you will exist involved in the interaction with clients to understand or present the requirements
  • Experience in Service Now ITIL process would exist an added advantage
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    Big data's growing pains | true questions and Pass4sure dumps


    By Lisa Harrington, Dr. Zac Rogers, Dale Rogers, Richard Sharpe and Susan Lacefield | From the Quarter 1 2019 issue

    Big data analytics implementations are up, but satisfaction with those implementations is down, according to a recent survey. Why are companies frustrated with their attempts to harness the power of big sets of supply chain data?

    We are at the birth of a supply chain technological revolution. Everything—from pallets and trucks to a bag of Romaine lettuce—has the potential to collect, store, and transmit information. If only companies could remove this data and feed it into analytics and simulated intelligence systems, they could execute better, faster decisions than ever before, driving down blow and increasing value. That, at least, is the vision of what gargantuan data analytics could achieve.

    But now, reality is birth to set in. As more companies try to wrap their arms around their "big data" and implement more tangled analytic tools, they are birth to realize that achieving this vision is difficult work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are moreover finding that the key data that they necessity is often missing or inaccurate. The swear of gargantuan data analytics is coming, but perhaps not as easily as initially thought. That was the main message conveyed by the results of the "Second Annual gargantuan Data Analytics Study" conducted by the analytics company Competitive Insights LLC; the consultancy lharrington group; CSCMP's Supply Chain Quarterly; and two prominent supply chain management schools, Arizona status University and Colorado status University.

    This annual study is designed to provide companies with a benchmark that they can employ to understand the current status of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of gargantuan data. The intent is to panoply the levels of progress that companies are making in addition to the obstacles impeding that progress.

    A survey was conducted in both 2017 and 2018 with readers of Supply Chain Quarterly, subscribers to a newsletter produced by Competitive Insights, and a contact list generated by Arizona status and Colorado status University researchers. A total of 125 usable responses were compiled for the 2018 survey, comparable to 2017's total of 133 usable responses.

    Here are the findings and some suggested best practices that could abet companies overcome their initial frustration and gain positive momentum with their gargantuan data analytics implementations.

    More implementations, less satisfaction

    A comparison of 2018's and 2017's results narrate an enthralling story. In both 2017 and 2018, they asked people: "How would you characterize your supply chain organization's maturity in regard to its employ of gargantuan data analytics?" (See device 1.) In general, the survey results panoply that more companies gain begun implementing gargantuan data analytics initiatives. There was a 14-percent expand in the number of gargantuan data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at every separate (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, device 1 moreover shows that there were fewer people reporting that their implementations were "transformational" or "advanced." Instead, the majority of respondents were in the "early" or "developing" stages of adoption, indicating that most companies are either noiseless conducting proof-of-concept testing or gain only rolled out initial implementations.

    Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? They accomplish not believe that firms are necessarily less successful with their gargantuan data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may gain evolved since terminal year. As more companies implement gargantuan data analytics in earnest, they are developing a better understanding of what it entails and how much farther they gain to go. In other words, companies now gain a more realistic assessment of where they actually tumble on the maturity curve and the extent of the obstacles they must overcome. The widespread excitement of 2017 has been replaced by the realization that, enjoy almost every separate recent technologies, it takes a lot of travail to execute gargantuan data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.

    We moreover perceive this perspective in respondent satisfaction with the data they accomplish have. In general, companies are less satisfied with the character and availability of their data in 2018 than they were in 2017. On average, respondents in 2018 were 4 percent less satisfied with their data availability, 8 percent less satisfied with data usability, and 7 percent less satisfied with their data's integrity than respondents in 2017. Satisfaction with data reliability moreover dropped 5 percent between 2018 and 2017. It seems unlikely that data character actually dropped from 2017 to 2018. Rather, they believe that as companies acquire deeper into their gargantuan data analytics implementations, they are becoming more aware of their existing data issues and gain a better understanding of the magnitude of the application and commitment required to address them.

    It stands to intuition that if respondents are less satisfied with the character of their data, they are going to exist less satisfied with their data analytics results as well. Indeed, the 2018 survey showed a slight drop in respondents' assessment of the realized benefits of their gargantuan data analytics efforts in 2018 relative to 2017. In both years, survey respondents were asked to employ a seven-point scale to quantify the impact they gain already realized from gargantuan data analytics in a variety of areas, such as profitability, inventory management, and visibility to total cost-to-serve. A score of 1 equals no impact, and 7 equals a transformative impact. device 2 shows a slight drop in perceived impact across every separate of the potential benefits in 2018 versus 2017.

    The nature of analytics matters

    When assessing the benefits that can exist expected from a gargantuan data analytics implementation, it is crucial to understand that there are many types of analytics. For the survey, they defined the different types of data analytics as follows:

  • Descriptive—what is happening/has happened
  • Diagnostic—why it's happening
  • Predictive—what will happen
  • Prescriptive—what should exist done
  • Cognitive—uses machine learning to narrate what should or could exist done
  • Survey respondents were again asked to select from a seven-point scale (where a score of 1 equals no use, and a score of 7 equals cumbersome use) the extent that their company currently uses each of those types of analytics to advocate supply chain decision making. On average, respondents gave descriptive analytics a score of 4.61 (indicating between "some use" and "regular use"), diagnostic analytics received a score of 4.02, predictive analytics a score of 3.16 (indicating between "some use" and "occasional use"), 3.56 for prescriptive analytics, and 2.27 (or "infrequent use") for cognitive analytics. (See device 3.) These scores are similar or slightly less than what was seen terminal year. The survey results bespeak that more sophisticated types of analytics are noiseless used less than more rudimentary methods in many real-life supply chains.

    Further investigation using regression analyses on the responses identified a significant correlation between the nature of analytics that firms employ and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider reach of benefits. device 4 shows that the employ of descriptive analytics is only correlated with improvements in customer service. This finding makes sense, as knowing what is happening in their supply chain can abet companies better inform their customers of the problems they are facing before these issues become visible to them. While employ of diagnostic analytics was slightly associated with require planning and highly correlated with collaboration, no correlation could exist create with any of the other realized benefits. (By "slight," they express that they are greater than 90 percent inescapable there is a correlation, instead of greater than 95 percent positive there is a correlation.) Predictive analytics, however, could be linked to many benefits, such as improvements in require planning, risk management, and collaboration.

    Somewhat counterintuitively, prescriptive analytics was not positively related with any of these realized benefits. In fact, it was slightly negatively correlated to improvements in require planning. It's possible that by the time firms acquire to the level of prescriptive analytics, they not only gain made some improvements to their require planning but moreover are more aware of their issues. Without the capabilities of more advanced analytics, they may gain misfortune addressing these issues which they now gain more insight into. In many ways, this negative correlation is a microcosm of the trends they perceive throughout the 2018 report. Firms are birth to implement more sophisticated protocols, and, in the process, are birth to realize the obstacles they gain yet to overcome.

    Finally, they create that the most helpful nature of analytics was cognitive, with a tough correlation to risk management and productivity and a slight correlation to customer service, visibility, and collaboration. In other words, the more sophisticated and forward-looking the nature of analytics that are being used, the more benefits companies are realizing.

    Similar results can exist observed when considering the types of software tools that companies are employing to execute their analytics. Survey respondents were again asked to employ a seven-point scale (where a score of 1 equals "no use," a score of 4 equals "some use," and a score of 7 signifies "heavy use") to measure the extent that their company currently uses a variety of analytics tools. Those tools included: Microsoft transcend or similar spreadsheet programs; operational point solutions (OPS) such as warehouse management systems and transportation management systems; advanced analytical tools associated with enterprise resource planning (ERP) systems; and trade intelligence (BI) tools. As was the case in terminal year's survey results, transcend spreadsheets continue to carry the day and are by far the most widely used analytics instrument with an middling score of 5.80, indicating "frequent use." OPS received an middling score of 4.64, and ERP and trade intelligence tools received scores of 3.97 and 3.88 respectively.

    The survey team moreover ran multiple analyses to perceive if there were any links between realized tangible gargantuan data analytical benefits and the employ of these platforms. (See device 5.) While Microsoft transcend was the most widely used of these platforms, it moreover was the least useful. In fact, no correlation was established between the employ of either transcend or an OPS as the primary gargantuan data analysis platform and any tangible improvement in customer service, require planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing gargantuan data are ineffective. They may exist useful for maintaining the status quo, but they are unlikely to lead to many benefits. Without a change in methods, it is unlikely firms will perceive a change in results.

    Conversely, although ERP and BI systems were the least used platforms, the regression analysis suggests that they were the most beneficial. ERP employ was significantly correlated with realized benefits in customer service, require planning, risk management, inventory management, visibility, and increased profitability. trade intelligence users reported the greatest levels of performance improvement in customer service, supply chain visibility, end-to-end supply chain collaboration, and overall productivity. These results seem to demonstrate that to execute gargantuan data analysis work, companies necessity to exist using the birthright tools.

    Still hope?

    Although most of the survey respondents gain only implemented the less sophisticated types of analytics, they are noiseless hopeful about the results that they are going to achieve. Companies reported that they expect moderately significant improvements in customer service, supply chain visibility, productivity, and profitability in the next 12 months. However, only 40 percent of survey respondents are planning to execute a temper to very big investment in cognitive analytics in the next 12 months, and only 52 percent blueprint to execute a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents blueprint to invest in descriptive analytics.

    The 2018 survey results point to a disconnect between reality and anticipated results. Companies seem to exist focusing their investments in baseline solutions but are hoping for results that are correlated to more sophisticated solutions.

    In spite of these discouraging signs, there is hope. Survey respondents generally reported facing fewer roadblocks to implementing gargantuan data analytics than they did terminal year. On a seven-point scale, every separate of the potential impediments that they asked about dropped from an middling score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See device 6.)

    After analyzing the 2017 data versus the 2018 data, they determined that managerial advocate was the key dissimilarity between this year and terminal year. Companies that had tough managerial advocate did not perceive getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to gargantuan data analytics implementation. They moreover did not believe that concerns about security risks posed an impediment to adoption. However, they did noiseless believe that talent acquisition, integrating siloed legacy systems, and gaining competency in recent tools were significant barriers to further implementations.

    The fact that more companies are reporting having the advocate of top management is an encouraging sign, as this is essential for continuing to propel successful gargantuan data analytics initiatives when they kisser difficulties or missteps. It moreover ensures that the implementations will receive the funding they necessity to gain the required tools, staff, and training.

    The road ahead

    It would exist simple to study at the results from this year's survey and feel discouraged. While the number of companies implementing gargantuan data analytics programs has increased, their satisfaction with the data they are working with and with the overall results of these initiatives has dropped. Companies seem frustrated with their necessity of progress and recur on investment.

    However, this fervor of frustration and even disillusionment is not surprising. It is actually fairly typical for the implementation of any recent technology, as described by the analyst group Gartner in its "Hype Cycle." Many technology experts gain observed that when a recent technology is introduced there is often a term of increasing hype and pile expectations. When the technology does not initially live up to those overinflated expectations, there is often a term that Gartner calls "the trough of disillusionment." During this period, companies' expectations for the technology drop rapidly as they proceed through the pang of actual implementation. Then, as companies learn what the technology can actually do, expectations tower again. During that time, companies regain some of the expectations that they lost in the trough of disillusionment, but they never again reach the height seen during the start of the hype cycle. The pang that many firms seem to exist experiencing with their gargantuan data analytics implementations is a normal, and often essential, portion of the maturity process.

    It's possible that gargantuan data analytics has entered that "trough of disillusionment," as companies realize how much travail they necessity to accomplish to cleanly up their data and expand their employ of additional types of analytics. To acquire reliable and trusted data, companies necessity to gain not just a central data repository, but moreover an executive-sponsored, cross-organizational approach to data collection and analysis. This process needs to factor in accountability, repeatability, and subject-matter validation. This takes time and work. To abet lead companies out of this "trough of disillusionment," perceive the sidebar on "Suggested best practices."

    Companies are moreover in the process of learning that the analytical tools they are currently using are not sophisticated enough to provide them with the timely, accurate, and specific insights needed to execute smart decisions. Companies necessity solutions that can quickly retort the following questions: What happened? Why did it happen? If they execute this change, what could happen? These tools moreover necessity to exist able to track the pecuniary and operational impacts of the changes that were made. These types of solutions are out there, but companies will necessity to execute investments in recent software and address the change management issues of fully utilizing the insights gained. They will moreover necessity to invest in their employees to execute positive they gain the skill sets required to travail with these systems.

    While the initial shine and optimism might gain worn off gargantuan data analytics, companies gain a more realistic assessment of the road ahead. They more clearly understand the gaps they kisser in data character and accessibility and the limitations of their existing tools. But gargantuan data analytics, combined with simulated intelligence methods, noiseless holds distinguished swear for the future. With the birthright level of executive advocate and investment, companies can reach the point where they gain the valid data that they necessity for analytics tools to execute better decisions more efficiently. But until then, companies must prepare themselves to travail their artery through a term of uncomfortable growing pains.


    1. Regression analysis is a set of statistical processes for estimating the relationship among variables. For example, regression analysis could exist used to establish whether there is a relationship between respondents who employ descriptive analytics and those who reported improvements in customer service.


    Suggested best practices

    The talent to gain the most value from gargantuan data analytics is a journey. You can accelerate your company's progress by following proven best practices that address the people involved, the processes and data employed, and the technology utilized.


  • Involve cross-functional teams, with key stakeholders from sales, finance, and marketing, early in any implementation effort. The solutions or root causes that gargantuan data analytics may reveal will impact multiple areas and not always exist confined to the supply chain. Likewise, exist positive to share any successes with the other functions involved.
  • Link your gargantuan data analytics efforts to your company's key initiatives. Using the tools to ameliorate something that is vital to your trade will abet you build momentum and gain advocate across the organization.
  • Make positive that you gain senior-level advocate and ideally a project champion for your gargantuan data analytics efforts. Garnering this advocate will exist easier if you can tie it back to pecuniary performance.
  • Process:

  • Avoid "one-off," separate design efforts. Recognize that gargantuan data analytics is not meant to exist a separate project. Instead, you are pile an ongoing organizational capability. Integrating siloed projects is costly and time-consuming. The cost of coordinating implementation efforts early on will pay off down the road.
  • Be intentional about the focus of a specific analytics effort. Clearly communicate what question the gargantuan data analytics project will hunt to retort and who the key stakeholders are. Otherwise you risk scope creep, with the project broadening to the point where it becomes unmanageable and difficult to measure.
  • Adopt a "crawl, walk, run" mentality. Start with a petite pilot project first. Getting small, achievable wins early will abet build momentum for bigger projects down the road.
  • Measure direct pecuniary impact. A key metric for judging the success of a gargantuan data analytics project is how much it contributed to the bottom line.
  • Data:

  • Gain cross-functional organizational consensus on what data sources should exist used.
  • Make positive that you employ the best sources of data and that the data itself is specific and accurate. This may involve the establishment of criterion operating procedures for data collection a few months prior to the start of an implementation.
  • Have sound data governance and repeatable data validation to ensure that the entire organization trusts the data.
  •  Technology:

  • Design solutions to exist used cross-functionally. Ultimately, gargantuan data analytics should exist applied across the organization, beyond the supply chain. execute positive the technology you employ has this cessation goal in mind.
  • Apply an agile development approach. As you implement gargantuan data analytics in more areas and to address more problems, your approach will change. execute positive that your technology can easily adjust to any changes.
  • Make positive that the technology is scalable.
  • Lisa Harrington is President of lharrington group LLC. Dr. Zac Rogers is an aide professor in the managment department at Colorado status University's College of Business.  Dale Rogers is a professor of logistics and supply chain management at Arizona status University's W. P. Carey School of Business. Richard Sharpe is CEO of Competitive Insights LLC, a provider of cost-to-serve and profit-analytical solutions. Susan Lacefield is Executive Editor of CSCMP’s Supply Chain Quarterly.

    lisa at at

    Dale.Rogers at

    rsharpe at

    After you comment, click Post. If you're not already logged in, you will exist asked to log in or register.

    Please enable JavaScript to view the comments powered by Disqus.

    We Want to Hear From You! They invite you to share your thoughts and opinions about this article by sending an e-mail to ?Subject=Letter to the Editor: Quarter 2019: gargantuan data's growing pains"> . They will publish selected readers' comments in future issues of CSCMP's Supply Chain Quarterly. Correspondence may exist edited for clarity or for length.

    Want more articles enjoy this? Subscribe to CSCMP's Supply Chain Quarterly.

    Microsoft (70-475): Designing and Implementing gargantuan Data Analytics Solutions | true questions and Pass4sure dumps

    MeasureUp’s 70-475: Designing and Implementing gargantuan Data Analytics Solutions practice test is designed to abet candidates prepare for and pass the Microsoft 70-475 exam.

    The Microsoft 70-475 exam is intended for data professionals who gain experience designing gargantuan data analytics solutions on Microsoft Azure. Candidates should exist able to design gargantuan data batch processing and interactive solutions as well as gargantuan data real-time processing solutions. Candidates should moreover exist able to design machine-learning solutions and create and manage end-to-end cloud analytics solutions.

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