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70-475 Design and Implement colossal Data Analytics Solutions

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70-475 exam Dumps Source : Design and Implement colossal Data Analytics Solutions

Test Code : 70-475
Test cognomen : Design and Implement colossal Data Analytics Solutions
Vendor cognomen : Microsoft
: 42 true Questions

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Microsoft Design and Implement Big

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

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

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

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


Watch Microsoft's MWC 2019 reside stream correct right here | killexams.com true Questions and Pass4sure dumps

MWC 2019

MWC 2019

Microsoft is making its first colossal appearance at mobile World Congress (MWC) in quite ages this yr, and you'll live 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 function "new innovations for the future of computing" with comments from Microsoft CEO Satya Nadella and HoloLens chief Alex Kipman.

which you can celebrate 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 signs point to a 2d iteration of the company's HoloLens augmented truth 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 they ought to toddle off of is a slick, cryptic teaser video posted to YouTube ultimate week by means of Kipman himself.

The traditional 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 condense with the U.S. militia, which could lead on the military to eventually purchase upwards of 100,000 HoloLens devices.

when you deem that the traditional 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 withhold in obligate deep neural networks to research objects and statistics without sending the comfort to the cloud. although, they forecast 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 advocate for its MWC 2019 keynote.

This withhold up may involve affiliate hyperlinks. notice their disclosure policy for extra particulars.


AMD Navi GPUs could notice colossal performance augment With Variable charge Shading Tech | killexams.com true Questions and Pass4sure dumps

A these days posted patent software means that AMD can live infusing a technology called Variable charge 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 mode around the identical time (a tape out is when a company infatuation AMD finalizes its semiconductor design before sending it off for manufacturing).

VSR is a rendering approach designed to diminish the tribulation 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 profit the most from this tech, the region your eyes are concentrated on the core. That talked about, VSR might conceivably live used in alternative routes for gaming on the desktop. This may especially live really useful at greater resolutions which are greater taxing on a GPU, equivalent to 4K, and eventually 8K if they want to peer that far down the street. AMD is not by myself in seeing the odds 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, effortless to withhold into sequel rendering mode enabled with the aid of Turing GPUs. It raises rendering efficiency and satisfactory by way 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 likewise live processed through a separate pixel shader operation. separate pixel shading operations can now live applied to a screen of pixels, permitting applications to effectively differ the shading charge in diverse areas of the monitor. Variable rate Shading can likewise live 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 live discarded earlier than the photograph is output to the VR headset.

there is actually widespread activity in VSR. concerning that, Microsoft likewise 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 exigency to wait and spot.

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Design and Implement colossal Data Analytics Solutions

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Reporting & Analytics - colossal Data Developer# 125072 | killexams.com true questions and Pass4sure dumps

eFinancialCareers jobs: Reporting & Analytics - colossal 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 amenable for providing innovative ways through maintaining lore of industry standard methodology, technology trends and product availability. Developing traffic and technical requirements through consultation with key client traffic partners and ensuring consistency to corporate standards for risk and security compliance.

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

    Duties:

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

    You should likewise live well versed with data design and hold ETL process experience.

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

    You Offer

  • You hold a degree or higher education in Information Technology or equivalent travail experience
  • You hold at least 3 to 4 years of undergo pile Bigdata environment
  • You hold demonstrable undergo with Data Engineering
  • You hold deep lore in Bigdata impala/Hadoop
  • You hold 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 live an advantage
  • Good interpersonal skills as you will live involved in the interaction with clients to understand or present the requirements
  • Experience in Service Now ITIL process would live an added advantage
  • For more information visit Technology Careers . eFinancialCareers
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    Big data's growing pains | killexams.com true questions and Pass4sure dumps

    Technology

    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 large sets of supply chain data?

    We are at the genesis 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 Take this data and feed it into analytics and synthetic intelligence systems, they could manufacture better, faster decisions than ever before, driving down consume and increasing value. That, at least, is the vision of what colossal data analytics could achieve.

    But now, reality is genesis to set in. As more companies try to wrap their arms around their "big data" and implement more involved analytic tools, they are genesis to realize that achieving this vision is arduous work. It takes significant investment in information technology (IT) systems as well as change and process management. Companies are likewise finding that the key data that they exigency is often missing or inaccurate. The engage of colossal 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 colossal 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 situation University and Colorado situation University.

    This annual study is designed to provide companies with a benchmark that they can employ to understand the current situation of supply chain data analytics and learn what analytical strategies organizations are adopting to harness the power of colossal data. The intent is to note 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 situation and Colorado situation 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 wait on companies overcome their initial frustration and gain positive momentum with their colossal data analytics implementations.

    More implementations, less satisfaction

    A comparison of 2018's and 2017's results declare an tantalizing story. In both 2017 and 2018, they asked people: "How would you characterize your supply chain organization's maturity in esteem to its employ of colossal data analytics?" (See design 1.) In general, the survey results note that more companies hold begun implementing colossal data analytics initiatives. There was a 14-percent augment in the number of colossal data implementations between 2017 and 2018, and far fewer people reported that they had not adopted supply chain analytics at every (only 10 percent in 2018 as opposed to 23 percent in 2017). And yet, design 1 likewise 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 hold only rolled out initial implementations.

    Why was there a drop off in the number of "transformational" and "advanced" responses in 2018 from 2017? They enact not believe that firms are necessarily less successful with their colossal data analytics implementations this year as opposed to 2017. Instead, responding firms' definition of "transformational" may hold evolved since eventual year. As more companies implement colossal data analytics in earnest, they are developing a better understanding of what it entails and how much farther they hold to go. In other words, companies now hold a more realistic assessment of where they actually Fall 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, infatuation almost every novel technologies, it takes a lot of travail to manufacture colossal data analytics useful. Companies, as a result, are more realistic about their progress along the maturity curve.

    We likewise notice this perspective in respondent satisfaction with the data they enact 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 likewise 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 accumulate deeper into their colossal data analytics implementations, they are becoming more awake of their existing data issues and hold a better understanding of the magnitude of the trouble 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 live 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 colossal 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 hold already realized from colossal 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. design 2 shows a slight drop in perceived impact across every of the potential benefits in 2018 versus 2017.

    The type of analytics matters

    When assessing the benefits that can live expected from a colossal 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 live done
  • Cognitive—uses machine learning to declare what should or could live 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 weighty use) the extent that their company currently uses each of those types of analytics to advocate supply chain conclusion 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 design 3.) These scores are similar or slightly less than what was seen eventual year. The survey results betoken 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 type of analytics that firms employ and the benefits that they report achieving.1 Generally, more sophisticated analytics are associated with a wider orbit of benefits. design 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 wait on 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 exact planning and highly correlated with collaboration, no correlation could live create with any of the other realized benefits. (By "slight," they value that they are greater than 90 percent certain there is a correlation, instead of greater than 95 percent certain there is a correlation.) Predictive analytics, however, could be linked to many benefits, such as improvements in exact 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 exact planning. It's feasible that by the time firms accumulate to the smooth of prescriptive analytics, they not only hold made some improvements to their exact planning but likewise are more awake of their issues. Without the capabilities of more advanced analytics, they may hold stitch addressing these issues which they now hold more insight into. In many ways, this negative correlation is a microcosm of the trends they notice throughout the 2018 report. Firms are genesis to implement more sophisticated protocols, and, in the process, are genesis to realize the obstacles they hold yet to overcome.

    Finally, they create that the most helpful type 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 type of analytics that are being used, the more benefits companies are realizing.

    Similar results can live observed when considering the types of software tools that companies are employing to effect 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 traffic intelligence (BI) tools. As was the case in eventual year's survey results, transcend spreadsheets continue to carry the day and are by far the most widely used analytics appliance with an medium score of 5.80, indicating "frequent use." OPS received an medium score of 4.64, and ERP and traffic intelligence tools received scores of 3.97 and 3.88 respectively.

    The survey team likewise ran multiple analyses to notice if there were any links between realized tangible colossal data analytical benefits and the employ of these platforms. (See design 5.) While Microsoft transcend was the most widely used of these platforms, it likewise was the least useful. In fact, no correlation was established between the employ of either transcend or an OPS as the primary colossal data analysis platform and any tangible improvement in customer service, exact planning, risk management, supply chain visibility, collaboration, or overall productivity. This demonstrates that for many companies, the current methods for analyzing colossal data are ineffective. They may live 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 notice 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, exact planning, risk management, inventory management, visibility, and increased profitability. traffic 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 look to demonstrate that to manufacture colossal data analysis work, companies exigency to live using the right tools.

    Still hope?

    Although most of the survey respondents hold 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 manufacture a qualify to very large investment in cognitive analytics in the next 12 months, and only 52 percent mode to manufacture a significant investment in predictive analytics. In contrast, almost two-thirds (63 percent) of survey respondents mode to invest in descriptive analytics.

    The 2018 survey results point to a disconnect between reality and anticipated results. Companies look to live 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 colossal data analytics than they did eventual year. On a seven-point scale, every of the potential impediments that they asked about dropped from an medium score between 5 (moderately significant) and 4 (neither significant nor insignificant) to scores between 4 and 3 (moderately insignificant). (See design 6.)

    After analyzing the 2017 data versus the 2018 data, they determined that managerial advocate was the key dissimilarity between this year and eventual year. Companies that had tough managerial advocate did not notice getting their firms to invest in additional software or hardware or understanding the value proposition as significant barriers to colossal data analytics implementation. They likewise 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 novel 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 shove successful colossal data analytics initiatives when they puss difficulties or missteps. It likewise ensures that the implementations will receive the funding they exigency to gain the required tools, staff, and training.

    The road ahead

    It would live effortless to peer at the results from this year's survey and feel discouraged. While the number of companies implementing colossal 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 look frustrated with their lack of progress and revert on investment.

    However, this passion of frustration and even disillusionment is not surprising. It is actually fairly typical for the implementation of any novel technology, as described by the analyst group Gartner in its "Hype Cycle." Many technology experts hold observed that when a novel technology is introduced there is often a age of increasing hype and pile expectations. When the technology does not initially live up to those overinflated expectations, there is often a age that Gartner calls "the trough of disillusionment." During this period, companies' expectations for the technology drop rapidly as they toddle through the stitch of actual implementation. Then, as companies determine what the technology can actually do, expectations surge 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 stitch that many firms look to live experiencing with their colossal data analytics implementations is a normal, and often essential, piece of the maturity process.

    It's feasible that colossal data analytics has entered that "trough of disillusionment," as companies realize how much travail they exigency to enact to immaculate up their data and expand their employ of additional types of analytics. To accumulate amenable and trusted data, companies exigency to hold not just a central data repository, but likewise 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 wait on usher companies out of this "trough of disillusionment," notice the sidebar on "Suggested best practices."

    Companies are likewise 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 manufacture smart decisions. Companies exigency solutions that can quickly answer the following questions: What happened? Why did it happen? If they manufacture this change, what could happen? These tools likewise exigency to live able to track the pecuniary and operational impacts of the changes that were made. These types of solutions are out there, but companies will exigency to manufacture investments in novel software and address the change management issues of fully utilizing the insights gained. They will likewise exigency to invest in their employees to manufacture certain they hold the skill sets required to travail with these systems.

    While the initial shine and optimism might hold worn off colossal data analytics, companies hold a more realistic assessment of the road ahead. They more clearly understand the gaps they puss in data character and accessibility and the limitations of their existing tools. But colossal data analytics, combined with synthetic intelligence methods, noiseless holds noteworthy engage for the future. With the right smooth of executive advocate and investment, companies can reach the point where they hold the cogent data that they exigency for analytics tools to manufacture better decisions more efficiently. But until then, companies must prepare themselves to travail their way through a age of uncomfortable growing pains.

    Notes:

    1. Regression analysis is a set of statistical processes for estimating the relationship among variables. For example, regression analysis could live 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 capacity to gain the most value from colossal 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.

     People

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

  • Avoid "one-off," separate design efforts. Recognize that colossal data analytics is not meant to live 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 colossal data analytics project will quest to answer 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 small pilot project first. Getting small, achievable wins early will wait on build momentum for bigger projects down the road.
  • Measure direct pecuniary impact. A key metric for judging the success of a colossal data analytics project is how much it contributed to the bottom line.
  • Data:

  • Gain cross-functional organizational consensus on what data sources should live used.
  • Make certain that you employ the best sources of data and that the data itself is specific and accurate. This may involve the establishment of standard 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 live used cross-functionally. Ultimately, colossal data analytics should live applied across the organization, beyond the supply chain. manufacture certain the technology you employ has this conclude goal in mind.
  • Apply an agile evolution approach. As you implement colossal data analytics in more areas and to address more problems, your approach will change. manufacture certain that your technology can easily appropriate to any changes.
  • Make certain 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 situation University's College of Business.  Dale Rogers is a professor of logistics and supply chain management at Arizona situation 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 lharringtongroup.com

    zac.rogers at colostate.edu

    Dale.Rogers at asu.edu

    rsharpe at ci-advantage.com

    After you comment, click Post. If you're not already logged in, you will live 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: colossal data's growing pains"> . They will publish selected readers' comments in future issues of CSCMP's Supply Chain Quarterly. Correspondence may live edited for clarity or for length.

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


    Microsoft (70-475): Designing and Implementing colossal Data Analytics Solutions | killexams.com true questions and Pass4sure dumps

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

    The Microsoft 70-475 exam is intended for data professionals who hold undergo designing colossal data analytics solutions on Microsoft Azure. Candidates should live able to design colossal data batch processing and interactive solutions as well as colossal data real-time processing solutions. Candidates should likewise live able to design machine-learning solutions and create and manage end-to-end cloud analytics solutions.

    Certification: This exam counts as credit toward the following certifications:Specialist



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