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70-776 Performing large Data Engineering with Microsoft Cloud Services

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70-776 exam Dumps Source : Performing large Data Engineering with Microsoft Cloud Services

Test Code : 70-776
Test name : Performing large Data Engineering with Microsoft Cloud Services
Vendor name : Microsoft
free pdf : 69 actual Questions

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Microsoft Performing large Data Engineering

faculty of Engineering college individuals collect hold of NSF profession Awards | killexams.com actual Questions and Pass4sure dumps

Two Michigan condition university desktop science and engineering college from the college of Engineering have obtained NSF profession Awards.

H. Metin Aktulgawill consume his career Award to strengthen algorithms and utility to aid computational scientists and massive statistics researchers tackle the challenges they puss when performing big-scale computations on parallel computing device programs. The 5-12 months, $500,000 accouter began in February 2019.

“establishing parallel application to execute efficaciously on high-end systems with many core processors, GPUs, and profound memory hierarchies may besides be an insurmountable,” Aktulga said. “during this mission, they focal point on computations involving sparse matrices and graphs as they seem in a few areas of huge data analytics and scientific computing. We goal to enhance a framework on the route to enable scientists and engineers to express their sparse matrix-primarily based solvers via an simple interface. Parallelization, performance optimization and productive entry to significant statistics sets would then be dealt with behind the scenes,”

Jiliang Tangwill consume his five-yr, $507,000 NSF profession provide, which started in March 2018, to enlarge the analytics of convivial networks. 

Tang pointed out clients who “like” or “block” messages are creating significant challenges to common community analysis.

“In today’s convivial programs, engagement between americans can besides be both advantageous and detestable in terms of blocked and unfriended clients,” Tang mentioned. “Networks cease up with both wonderful and destitute links, called ‘signed networks,’ which occupy distinctive residences and concepts from unsigned ones. This poses colossal challenges to common network evaluation, so their undertaking will allow the evaluation of networks with detestable links and a variety of records-suggestions areas. the new algorithms will champion in additional complete modeling, measuring and mining.”

Aktulga and Tang are the seventeenth and 18th Engineering faculty to receive NSF profession Awards seeing that 2010. NSF career Awards, which are amongst NSF’s most prestigious honors, aid junior school who exemplify the role of instructor-scholars via miraculous analysis and training. 


Cloudwick Collaborates with Pepperdata to do positive SLAs and performance are Maintained for AWS Migration carrier | killexams.com actual Questions and Pass4sure dumps

Pepperdata provides Pre- and achieve up-Migration Workload analysis, application performance evaluation and SLA Validation for Cloudwick AWS Migration valued clientele

SAN FRANCISCO, March 27, 2019 /PRNewswire/ -- Strata records conference - booth 926 -- Pepperdata, the leader in massive information application efficiency administration (APM), and Cloudwick, leading issuer of digital enterprise functions and options to the global 1000, today introduced a collaborative offering for organisations migrating their large records to Amazon web functions (AWS). Pepperdata provides Cloudwick with a baseline of on-premises efficiency, maps workloads to premiere static and on-demand instances, diagnoses any concerns that arise outright the route through migration, and assesses efficiency after the movement to do inescapable the selfsame or superior efficiency and SLAs.

View pictures

"The largest challenge for organizations migrating massive records to the cloud is making inescapable SLAs are maintained while not having to entrust materials to completely re-engineer functions," talked about Ash Munshi, Pepperdata CEO. "Cloudwick and Pepperdata do inescapable workloads are migrated efficiently by inspecting and organising a metrics-primarily based efficiency baseline."

"Migrating to the cloud devoid of searching at the efficiency facts first is perilous for groups and if a migration is not achieved correct, the complaints from lines of enterprise are unavoidable," said token Schreiber, typical manager for Cloudwick. "with out Pepperdata's metrics and evaluation earlier than and after the migration, there is not any route to prove efficiency tiers are maintained within the cloud."

For Cloudwick's AWS Migration capabilities, Pepperdata is achieve in on customers' present, on-premises clusters — it takes under 30 minutes — and instantly collects over 350 real-time operational metrics from applications and infrastructure components, including CPU, RAM, disk I/O, and network utilization metrics on each job, assignment, person, host, workflow, and queue. These metrics are used to research efficiency and SLAs, accurately map workloads to appropriate AWS circumstances, and provide impregnate projections. once the AWS migration is comprehensive, the equal operational metrics from the cloud are accrued and analyzed to investigate efficiency outcomes and validate migration success.

To be taught greater, cease via the Pepperdata booth (926) at Strata data conference March 25-28 at Moscone West in San Francisco.

extra info

About PepperdataPepperdata (https://pepperdata.com) is the leader in large data utility efficiency management (APM) options and services, fixing utility and infrastructure issues outright over the stack for builders and operations managers. The enterprise companions with its valued clientele to provide confirmed products, operational adventure, and profound talents to carry predictable efficiency, empowered users, managed expenses and managed boom for his or her huge data investments, each on-premise and in the cloud. leading agencies enjoy Comcast, Philips Wellcentive and NBC time-honored depend upon Pepperdata to convey large facts success. centered in 2012 and headquartered in Cupertino, California, Pepperdata has attracted government and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata traders encompass Citi Ventures, Costanoa Ventures, Signia assignment partners, Silicon Valley facts Capital and Wing venture Capital, together with main excessive-profile particular person investors. For greater suggestions, visit www.pepperdata.com.

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Three consultants on huge information Engineering | killexams.com actual Questions and Pass4sure dumps

Key Takeaways
  • learn about large data systems from discipline import specialists from Microsoft, IBM, and Amazon web capabilities
  • Technical challenges in purposes in line with the distinctive massive records dimensions: velocity, volume, veracity, range
  • build really edifying microservices that address the specific units of massive statistics requirements
  • changing the route they occupy interaction with facts to empower americans to obtain suggestions and do businesses more advantageous
  • Scalability, elasticity and automated resiliency of massive facts techniques
  • this text first appeared in IEEE application magazine. IEEE utility presents strong, peer-reviewed assistance about concomitant strategic expertise concerns. to fulfill the challenges of operating respectable, bendy firms, IT managers and technical leads reliance on IT professional for state-of-the-art options.

    dealing with the V's of huge facts Clemens Szyperski

    "large information" is a fascinating time period. americans occupy used it to elucidate various phenomena, regularly characterizing it based on a few v's, beginning with the natural pace, volume, and range. other dimensions were introduced, similar to veracity (the records's degree of truthfulness or correctness). In essence, massive information is characterised as a extreme bar on outright these dimensions. information arrives at elevated fees, looks in massive quantities, fragments into ever greater manifestations, and nonetheless should meet extreme much expectations.

    Engineering methods that meet the sort of large spectrum of necessities are not meaningful as such. in its place, you ought to slender the focus and request what the inescapable system to be built is meant to handle. for example, the service I labor on (Azure circulation Analytics, a platform service in the Azure cloud) specializes in quicken because it supports stream and sophisticated adventure processing the consume of temporal operators (up to 1 Gbyte/s per streaming job). volume, in the kindly of condition and reference datasets held in memory, is massive too, but in methods quite distinct from mass storage or batch-processing systems. in the presence of latency expectations (end-to-conclusion latencies within the low seconds) and inner restarts to fill frailty tolerance requirements, veracity comes to the fore. for example, these days output meets an at-least-once bar, but exactly once could be first-rate and is difficult given the diversity (oh-oh, an additional v!) of supported output targets. speaking about range: anyway the richness in statistics sources and aims, the nature of very longrunning move-processing jobs additionally requires flexibility in coping with evolving schema and a army of facts codecs.

    or not it's charming to verify the technical challenges borne with the aid of valuable combinations of requirements in velocity, volume, veracity, variety, and other dimensions. youngsters, to be greater than charming, the mixtures should tackle inescapable audiences' wants. Given the impossibility of meeting maximal necessities in outright dimensions, large statistics, more than another engineering category I've encountered, faces a deeply fragmented audience. From ordinary hard-core distributedsystems builders, to information builders, to information architects, to information scientists, to analysts, to builders of higher end-to-conclusion options in spaces such because the internet of issues, the checklist is lengthy.

    simply as maxing out on outright dimensions is unimaginable, or not it's unimaginable to fill outright these audiences equally well with a sole product or little set of products. as an example, they occupy now designed Azure circulate Analytics to be high-stage, with a declarative language as its leading interface, and to serve a large set of customers who don't seem to be distributed-systems builders. A service that is highlevel and composable with many other functions (like outright platform carrier necessity to be) mustn't expose artifacts of its internal fault-tolerance suggestions. This leads to necessities of at-least-as soon as (or, ideally, precisely once) start, repeatability, and determinism. These requirements aren't inescapable to huge records however constantly circle into plenty more durable to address in the event you're dealing with the dimensions of large statistics.

    So, a large a fragment of the engineering problem, and one cost tackling in ahead-searching research, is to assemble bigger huge information solutions (reminiscent of capabilities) out of composable points to reduce the elevated can impregnate of engineering these solutions. beginning with the textile to exploit substances, the style is pointing towards cloud oceans of containers-relocating from (digital) computer to method-level abstractions. Even at this degree, challenges abound if they necessity to map labor elope on behalf of distinctive tenants onto a sole such ocean. (Container oceans are the natural substances to drain your facts lakes into!) On rectify of such infrastructure, they ought to address the core challenges of affinitizing computations to the predominant useful resource. That resource might possibly be storage hierarchies or network means and may require either extensive distribution for load balancing or collocation for entry effectivity.

    Given such a cloth, they then ought to systematically construct incredibly really expert microservices that tie quite a lot of "knots" by addressing inescapable units of necessities. simply as with components, where they may occupy hoped for the definitive set of constructing blocks from which to compose outright functions, they may hope for a closed or essentially closed set of microservices that may be the definitive platform for composing huge data options. that's not likely to circle up-just as it failed to occur for accessories.

    during this advanced space, they want research into improved the perquisite route to manage resources (oceans) to ply contradictory requirements of collocation, consistency, and distribution. Abstractions of homogeneity extinguish down as containers grow to be allotted on hardware hierarchies and utility hierarchies with networking infrastructure it really is far from most desirable crossbar switches. If this weren't adequate, the should technique labor on behalf of possibly malicious or collectively adverse tenants requires profound safety and privateness isolation whereas retaining fl exible aid allocation and warding off layers of inner useful resource fragmentation (a source of primary resource inefficiency). Such fragmentation is traditionally the case if you betide to depend on isolation at the digital-computing device-stack or hardware cluster tiers.

    nowadays, we're a tiny bit midway during the analysis journey I simply sketched, with the aid of constructing platform capabilities that focus on individual units of traits, that compose with each other, and that in aggregate can meet a number of needs. youngsters, these services are the manufactured from a few competing efforts, leading to overlapping capabilities, often restrained composability, and confusion for those that necessity to construct solutions. simply within the realm of streaming technologies, they haven't simplest a number of open source applied sciences, comparable to Apache Storm and Apache Spark Streaming, but besides the a variety of proprietary applied sciences present in the common public-cloud choices. Azure circulation Analytics is only one of the latter. This richness of choice will proceed to be with us for quite a while, leaving such methods' clients with a enmesh 22 situation of option.

    altering How They engage with statistics Martin Petitclerc

    there are many technologies for large information engineering, and nobody know-how matches outright wants. an considerable change exists between tuning a gadget for a specific dataset (repeating the equal jobs) and having a gadget that tunes itself on exact (advert hoc) on the groundwork of distinct datasets and diverse queries in opposition t them. because the extent, velocity, and diversity of facts develop, the purpose is to no longer simply address greater information however besides locate how you can nick back the human intervention integral to collect the favored suggestions. Rule-based tactics-for example, ETL (extract, transform, and cargo)-aren't sustainable. They must alternate how they interact with the data.

    because the volume of records grows, the volume of lore assistance grows. outright lore items of suggestions aren't equally faultfinding to every person, and their cost may change over time. anything unimportant today may circle into faultfinding day after today, whereas different items of tips (as an example, protection breaches) are always vital. or not it's about getting the appropriate piece of counsel at the rectify time.

    at present, they ply these challenges through bundling different technologies for diverse wants-as an example, ordinary relational databases with emerging large statistics applied sciences. nevertheless, these programs aren't getting more convenient however occupy become greater tangled to advance, tune, and preserve, multiplying the technical challenges.

    Involving cognitive systems in outright phases of the facts manner is how to reduce human intervention. it be besides a route to hyperlink the statistics to clients' initiatives, targets, and dreams, defining outright collectively the user's present hobby within the statistics or the consumer's context for the system.

    methods that can remember those projects, ambitions, and desires and what's imperative over time will more quite simply serve clients' day by day wants for statistics, tips, and statistics. Such methods won't overload clients with immaterial or unimportant issues. as an example, esteem getting a abstract each morning about the entire changes you deserve to comprehend related to the latest week's creation ambitions. This suggestions includes root cause analysis and motion concepts on divergences, with occupy an repercussion on analyses detailing how each of those moves would repercussion the outcome.

    Such techniques may still empower everyone to understand data with out them having to circle into a lore scientist or an IT person. This contains simplifying complicated projects comparable to becoming a member of structured and unstructured earnings statistics to compare consumer sentiment with earnings figures, including their correlation over time.

    yet another such task is semiautomated statistics cleaning that applies a group of germane actions on the necessary facts at the required time. here is probably improved than having the IT folk achieve together a large amount of information that may on no account be used since the clients' wants exchange before the information is even competent. additionally, information cleansing can not seize position in a black-box manner, and facts lineage is vital in order that the clients can be awake what become carried out, why, and the route the transformation affected the records.

    The concept is not to substitute records scientists but to free them from helping basic actions and allow them to focal point on labor having bigger value to their businesses. as an instance, they may construct a extra accurate model to compute future coverage claims that contains climate alternate counsel. everyone outright through the organization might then consume this mannequin to duty forecasts.

    privacy can be a challenge for such information evaluation power, as the amount of attainable records grows. for example, attackers might still reconstruct assistance in some route even though privateness turned into blanketed at distinct particular person access features. They might hyperlink geospatial and temporal records to different statistics and correlate outright the facts to identify an entity (equivalent to someone).

    The analysis group should seat of attention on simplifying the handling of records so that it's greater contextual and on demand, devoid of requiring IT intervention at outright levels of the procedure. The community besides necessity to check how cognitive programs can empower outright types of users in an atmosphere by which the quantity, speed, and orbit of data are constantly growing to be. crucial research areas include consumer interplay with facts; information lineage; automation; visualization; structured and unstructured facts; information manipulation and transformation; instructing users about findings; and the capability to extend, tune, and additional prolong such systems.

    today, the focus on massive statistics looks to in common contain performance, but empowering americans to straight away gather information is what is going to do organizations greater positive.

    coping with the Scaling Cliff Roger Barga

    massive data and scalability are two of the most well-liked and most considerable themes in present day speedy-transforming into facts analytics market. not most effectual is the cost at which they accumulate data starting to be, so is the diversity of sources. Sources now span the spectrum from ubiquitous cell devices that create content equivalent to blog posts, tweets, social-network interplay, and pictures, to applications and servers that normally log messages about what they're doing, to the emerging cyber web of things.

    massive facts methods must be in a position to scale impulsively and elastically, every time and anyplace needed, throughout dissimilar datacenters if necessity be. but what finish they really imply by means of scalability? A tackle is considered scalable if expanding the obtainable components effects in elevated performance proportional to the resources brought. elevated efficiency often ability serving more units of labor however can additionally be considerable handling greater gadgets of work, comparable to when records sets develop.

    you could scale up through including more supplies to existing servers or scale out by adding new independent computing components to a device. but ultimately you are going to elope out of larger packing containers to purchase, and including supplies will fail to further back advancements-you're going to occupy elope off the edge of the scaling cliff. Scaling cliffs are inevitable in huge statistics techniques.

    a tremendous challenge in reaching scalability and the haphazard to propel scaling cliffs out so far as possible is efficient aid administration. you can shard your facts, leverage NoSQL databases, and consume MapReduce for statistics processing unless the cows further domestic, however decent design is the only technique to do inescapable efficient aid administration. efficient design can add greater scalability to your tackle than including hardware can. This isn't confined to any selected tier or element; you must esteem aid administration at each and every stage, from load balancers, to the user interface layer, to the manage airplane, outright of the approach to the lower back-conclusion facts shop. here are opt for design principles for aid administration to obtain elevated scalability.

    Asynchronous versus SynchronousTime is probably the most effectual resource in a huge facts system, and each time slice a thread or system uses is a limited resource that a further cannot use. Performing operations asynchronously will nick the time a server is dedicated to processing a request. Servers can then queue long-operating operations for completion later by using a divorce system or thread pool.

    sometimes, a gadget ought to discharge operations synchronously, corresponding to verifying that an operation turned into a success to be inescapable atomicity. cautiously differentiate between system calls that should be processed synchronously and calls that can besides be written to an intent log and processed asynchronously. This precept can additionally eliminate "sizzling spots" in a large information device since it makes it possible for idle servers to "steal" labor from the intent log of a server beneath a elevated load.

    dealing with Contentious ResourcesAll systems possess finite actual substances; competition for these supplies is the basis intuition behind outright scalability issues. gadget throttling as a result of inadequate memory, garbage assortment, or inadequate file handles, processor cycles, or network bandwidth is the harbinger of an impending scaling cliff.

    A design precept is to no longer consume a contentious resource except fully integral, but when you necessity to consume it, acquire it as late as possible and unencumber it as soon as feasible. The less time a manner uses a useful resource, the earlier that aid should be purchasable to an extra manner. assessment code to do positive that contentious elements are back to the pool inside a hard and hasty time length. This design can start with quick SSL (comfortable Sockets Layer) termination at the load balancer. Hardware load balancers occupy crypto playing cards that can terminate SSL efficaciously in hardware and decrease the front-conclusion server load by using as a edifying deal as 40 p.c. The quick SSL termination will besides boost client performance. you could supervene this principle throughout the device layers.

    Logical PartitioningLogically partition components and activities outright the route through the gadget, and minimize the relationships between them. Partitioning actions can assist ease the tribulation on high-cost supplies. A top-quality rehearse is to logically partition your application between the proxy or person interface layer, ply airplane layer, and records airplane layer. youngsters logical separation does not mandate actual separation, it permits actual separation, and you may scale your tackle across machines. by means of minimizing the relationships between substances and between activities, you reduce the risk of bottlenecks as a consequence of one participant of a relationship taking longer than the other.

    Partitioning besides means that you can establish metrics and measure utilization at every layer. A entrance-end proxy layer that handles incoming requests could optimum be optimized for transactions per second, and the control airplane that manages operations could finest be optimized for CPU utilization, whereas the storage airplane could greatest be optimized for I/O operations per 2nd. This permits you to do inescapable your device is balanced, and not using a sole layer providing a bottleneck or an overabundance of components, the latter of which may cease up in underutilization or achieve pressure on different layers in the gadget.

    State CachingEmploy a state-caching fleet. If at outright possible, avoid holding state, which consumes effectual resources and complicates the potential to scale out. although, every so often you occupy to retain condition between calls or invoke carrier-degree agreements. condition isn't held via a sole resource as a result of that raises the probability of resource contention.

    So, a pattern supervene is to replicate condition across servers within the equal logical layer. should still the server further under load and be a point of aid competition, different servers in the equal logical layer can proceed the session through the consume of the condition in their cache. youngsters, peer-to-peer blab protocols can shatter down at massive scale, so a little (log N) dedicated caching fl eet is required. each server persists condition to a sole server within the caching fl eet, which then disseminates this throughout a quorum in the fleet. These servers can lazily propagate condition to servers within the logical layer in an efficient and scalable method.

    Divide and ConquerAt some factor, outright massive facts methods will encounter a scaling cliff that can not be engineered around. The handiest motel is the time-confirmed approach of divide and overcome: making an issue less demanding to resolve by means of dividing it into smaller, greater manageable steps. simply as your large statistics device is logically partitioned, probably into microservices, you create a divorce specimen of your system to achieve massive scale.

    automated ResiliencyThere are many open challenges on the road to more advanced and scalable large data programs. One problem that warrants further analysis is automatic resiliency. A smartly-designed large statistics device may besides be resilient enough to resist the unexpected want of one or more computing resources. however a very resilient gadget requires each first rate design and repair-stage usher to instantly become awake of and substitute situations that occupy failed or become unavailable. When a brand new illustration comes online, it'll seize into account its duty in the device, configure itself, find its dependencies, provoke condition recuperation, and start handling requests automatically.

    concerning the Authors

    Clemens Szyperski is the neighborhood engineering manager for the Azure movement Analytics platform carrier in the Microsoft cloud. Contact him at clemens.szyperski@microsoft.com.

    Martin Petitclerc is a senior software architect at IBM Canada for Watson Analytics. Contact him at martin.petitclerc@ca.ibm.com.

    Roger Barga is generic supervisor and director of progress for Amazon Kinesis information-streaming services at Amazon net services. Contact him at rsbarga@gmail.com.

    this text first regarded in IEEE software journal. IEEE utility presents solid, peer-reviewed suggestions about trendy strategic technology considerations. to fill the challenges of operating respectable, resilient agencies, IT managers and technical leads import on IT professional for state-of-the-paintings solutions.


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    Cloudwick Collaborates with Pepperdata to Ensure SLAs and Performance are Maintained for AWS Migration Service | killexams.com actual questions and Pass4sure dumps

    Pepperdata Provides Pre- and Post-Migration Workload Analysis, Application Performance Assessment and SLA Validation for Cloudwick AWS Migration Customers

    SAN FRANCISCO, March 27, 2019 /PRNewswire/ -- Strata Data Conference - Booth 926 -- Pepperdata, the leader in large data Application Performance Management (APM), and Cloudwick, leading provider of digital trade services and solutions to the Global 1000, today announced a collaborative offering for enterprises migrating their large data to Amazon Web Services (AWS). Pepperdata provides Cloudwick with a baseline of on-premises performance, maps workloads to optimal static and on-demand instances, diagnoses any issues that arise during migration, and assesses performance after the scamper to ensure the selfsame or better performance and SLAs.

    View photos

    "The biggest challenge for enterprises migrating large data to the cloud is ensuring SLAs are maintained without having to consecrate resources to entirely re-engineer applications," said Ash Munshi, Pepperdata CEO. "Cloudwick and Pepperdata ensure workloads are migrated successfully by analyzing and establishing a metrics-based performance baseline."

    "Migrating to the cloud without looking at the performance data first is risky for organizations and if a migration is not done right, the complaints from lines of trade are unavoidable," said token Schreiber, common Manager for Cloudwick. "Without Pepperdata's metrics and analysis before and after the migration, there is no route to prove performance levels are maintained in the cloud."

    For Cloudwick's AWS Migration Services, Pepperdata is installed on customers' existing, on-premises clusters — it takes under 30 minutes — and automatically collects over 350 real-time operational metrics from applications and infrastructure resources, including CPU, RAM, disk I/O, and network usage metrics on every job, task, user, host, workflow, and queue. These metrics are used to anatomize performance and SLAs, accurately map workloads to appropriate AWS instances, and provide cost projections. Once the AWS migration is complete, the selfsame operational metrics from the cloud are collected and analyzed to assess performance results and validate migration success.

    To learn more, cease by the Pepperdata booth (926) at Strata Data Conference March 25-28 at Moscone West in San Francisco.

    More Info

    About PepperdataPepperdata (https://pepperdata.com) is the leader in large data Application Performance Management (APM) solutions and services, solving application and infrastructure issues throughout the stack for developers and operations managers. The company partners with its customers to provide proven products, operational experience, and profound expertise to deliver predictable performance, empowered users, managed costs and managed growth for their large data investments, both on-premise and in the cloud. Leading companies enjoy Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver large data success. Founded in 2012 and headquartered in Cupertino, California, Pepperdata has attracted executive and engineering talent from Yahoo, Google, Microsoft and Netflix. Pepperdata investors include Citi Ventures, Costanoa Ventures, Signia Venture Partners, Silicon Valley Data Capital and Wing Venture Capital, along with leading high-profile individual investors. For more information, visit www.pepperdata.com.

    Story continues


    Amazon Web Services, Google Cloud, and Microsoft Azure unite NSF’s large Data Program | killexams.com actual questions and Pass4sure dumps

    January 27, 2017

    The National Science Foundation (NSF) announces the participation of cloud providers, including Amazon Web Services (AWS), Google, and Microsoft, in its flagship research program on large data, faultfinding Techniques, Technologies and Methodologies for Advancing Foundations and Applications of large Data Sciences and Engineering (BIGDATA). AWS, Google, and Microsoft will provide cloud credits/resources to qualifying NSF-funded projects, enabling researchers to obtain access to state-of-the-art cloud resources.

    The BIGDATA program involves multiple directorates at NSF, as well as the Office of financial Research (OFR), and anticipates funding up to $26.5 million, theme to availability of funds, in Fiscal Year (FY) 2017. Additionally, AWS, Google, and Microsoft will provide up to $9 million (up to $3 million each) in the configuration of cloud credits/resources for projects funded through this solicitation.

    This novel collaboration combines NSF’s relish in developing and managing successful large, diverse research portfolios with the cloud providers’ proven track records in state-of-the-art, on-demand, cloud computing. It besides builds upon the shared interests of NSF and the cloud providers to accelerate progress in research and innovation in large data and data science—pivotal areas that are expected to result in tremendous growth for the U.S. economy.

    The BIGDATA program encourages experimentation with actual datasets; demonstration of the scalability of approaches; and progress of evaluation plans that include evaluation of scalability and performance among competing methods on benchmark datasets—all of which will require significant storage, compute, and networking resources, which can be provided by the cloud vendors through their participation. 

    Proposals requesting cloud credits/resources must adhere to a 70:30 split between NSF funding and cloud resources, respectively, and must not request less than $100,000 for cloud requests. Thus, if a project requests $700,000 in NSF funds, then it may request a maximum of $300,000 in cloud credits/resources from one of AWS, Google, or Microsoft, or a minimum of $100,000. This minimum budget requirement underscores  key objectives of the BIGDATA program, which include supporting experimentation with data and studying data scaling issues.

    Proposal submissions are due March 15, 2017 through March 22, 2017 (and no later than 5 p.m. submitter’s local time on March 22nd).  outright those interested in submitting a proposal to the BIGDATA program should advert to the solicitation for details. outright proposals that meet NSF requirements will be reviewed through NSF’s merit review process. For proposals that request cloud resources, reviewers will additionally be asked to evaluate: (1) the appropriateness of the requested use; (2) whether the specific consume of cloud resources has been adequately justified through an annual usage plan; and (3) the assess of the amount of resources needed and the corresponding resource request budget (in dollars). The requests for cloud resources should not only include resources required for the experimentation phase, but besides for usage over the duration of the project (e.g., software progress and testing and code debugging).

    We are excited to offer this break and peruse forward to the response of the national large data and data science research community!

    NSF Program Contact: Chaitan Baru, cbaru@nsf.gov

    The National Science Foundation (NSF) is an independent federal agency that supports fundamental research and education across outright fields of science and engineering. In fiscal year (FY) 2019, its budget is $8.1 billion. NSF funds gain outright 50 states through grants to nearly 2,000 colleges, universities and other institutions. Each year, NSF receives more than 50,000 competitive proposals for funding and makes about 12,000 new funding awards.

    mail icon Get word Updates by Email 

    Useful NSF Web Sites:NSF Home Page: https://www.nsf.govNSF News: https://www.nsf.gov/news/For the word Media: https://www.nsf.gov/news/newsroom.jspScience and Engineering Statistics: https://www.nsf.gov/statistics/Awards Searches: https://www.nsf.gov/awardsearch/


    Industrial cloud historian for large data | killexams.com actual questions and Pass4sure dumps

    Invensys releases bundled data historian and reporting package. Aims at reduced implementation time and costs, improves on-demand performance. Video: Maryanne Steidinger explains its progress strategy.

    In the video, Maryanne Steidinger explains how the new cloud service was developed.Invensys has released a new, cloud-hosted Wonderware Historian Online Edition designed to provide customers a safe mechanism to participate more plant data with their workers while lowering their IT burden. pile on a foundation of more than 70,000 Wonderware Historian licenses, the company’s new Historian Online Edition offering can aid reduce implementation time, provides universal access, and delivers alternative pricing models for expanded industry use.

    This innovative, SaaS (software as a service) offering uses a multi-tier Historian database architecture, storing data from one or more local plant-level Wonderware Historians onto a cloud-hosted, enterprise-wide instance. Data flows only one way—from the local historians to the online historian—and it is protected from cyber intrusion so it can safely be made available to more workers for better troubleshooting, reporting, and analytics. The solution leverages Windows Azure cloud services from Microsoft Corp., so there is no software to install or set up, saving on valuable IT resources and reducing capital requirements.

    “Our new Wonderware Historian Online Edition is a revolutionary route of accessing and using real-time data on demand,” said Rob McGreevy, vice president, information, asset and operations software for Invensys. “Providing a hosted historian simplifies set-up, installation and ongoing maintenance, and besides improves usability for the cease users by safely and securely making the information available wherever and whenever needed. Users can scale as their needs grow, without having to worry about infrastructure, hardware or software costs, upgrades or support.”

    The solution leverages Windows Azure cloud services from Microsoft Corp., so there is no software to install or set up, saving on valuable IT resources and reducing capital requirements.This service will be offered as a yearly subscription, based on the number of users accessing the data. Reporting and analytics are delivered to the historian online edition through benchmark tools, including Invensys’ desktop reporting and analysis client, Wonderware Historian Client, along with its Wonderware SmartGlance mobile reporting solution. System users can view the data via multiple devices, including desktop PCs, laptops, tablets, and smart phones.

    The Wonderware Historian Online Edition is the first commercial offering from the Invensys-Windows Azure relationship, whereby the two companies jointly develop manufacturing operations software that can be hosted on the Windows Azure platform.

    “Windows Azure is a scalable, resilient cloud platform, and Invensys’ introduction of its Wonderware Historian Online Edition on Windows Azure demonstrates the value industrial firms can gain from using a platform that removes the tribulation of requiring expensive IT infrastructure to bring new products quickly online,” said Dewey Forrester, senior director, trade progress and evangelism at Microsoft.

    Edited by Peter Welander, pwelander@cfemedia.com

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