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70-776 Performing Big Data Engineering with Microsoft Cloud Services

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

Test Code : 70-776
Test Name : Performing Big Data Engineering with Microsoft Cloud Services
Vendor Name : Microsoft
: 69 Real Questions

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Microsoft Performing Big Data Engineering

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

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

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

“establishing parallel application to execute efficaciously on high-end systems with many core processors, GPUs, and deep memory hierarchies may also 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 way to enable scientists and engineers to express their sparse matrix-primarily based solvers via an easy interface. Parallelization, performance optimization and productive entry to significant statistics sets would then be dealt with behind the scenes,”

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

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

“In today’s social programs, engagement between americans can also be both advantageous and bad in terms of blocked and unfriended clients,” Tang mentioned. “Networks end up with both wonderful and poor links, called ‘signed networks,’ which have 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 bad links and a variety of records-suggestions areas. the new algorithms will support 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, help junior school who exemplify the role of instructor-scholars via miraculous analysis and training. 


Cloudwick Collaborates with Pepperdata to make sure SLAs and performance are Maintained for AWS Migration carrier | killexams.com Real Questions and Pass4sure dumps

Pepperdata provides Pre- and put 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 all the way through migration, and assesses efficiency after the movement to make certain the same or superior efficiency and SLAs.

View pictures

"The largest challenge for organizations migrating massive records to the cloud is making certain SLAs are maintained while not having to commit materials to completely re-engineer functions," talked about Ash Munshi, Pepperdata CEO. "Cloudwick and Pepperdata make certain 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 dangerous for groups and if a migration is not achieved correct, the complaints from lines of enterprise are unavoidable," said Mark Schreiber, normal manager for Cloudwick. "with out Pepperdata's metrics and evaluation earlier than and after the migration, there is not any way to prove efficiency tiers are maintained within the cloud."

For Cloudwick's AWS Migration capabilities, Pepperdata is put 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 charge 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 big data utility efficiency management (APM) options and services, fixing utility and infrastructure issues all over the stack for builders and operations managers. The enterprise companions with its valued clientele to provide confirmed products, operational adventure, and deep 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 like 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 Real Questions and Pass4sure dumps

Key Takeaways
  • learn about big data systems from discipline count 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 good microservices that address the specific units of massive statistics requirements
  • changing the way they have interaction with facts to empower americans to obtain suggestions and make 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 contemporary strategic expertise concerns. to fulfill the challenges of operating respectable, bendy firms, IT managers and technical leads rely 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 have used it to explain 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 excessive bar on all these dimensions. information arrives at high fees, looks in massive quantities, fragments into ever greater manifestations, and nonetheless should meet excessive great 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 ask what the certain system to be built is meant to handle. for example, the service I work on (Azure circulation Analytics, a platform service in the Azure cloud) specializes in speed because it supports stream and sophisticated adventure processing the use of temporal operators (up to 1 Gbyte/s per streaming job). volume, in the kind of state 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 satisfy fault 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 multitude 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 certain audiences' wants. Given the impossibility of meeting maximal necessities in all 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 all dimensions is unimaginable, or not it's unimaginable to satisfy all these audiences equally well with a single product or small set of products. as an example, they have 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 all platform carrier need 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 certain to huge records however constantly turn into plenty more durable to address in the event you're dealing with the dimensions of large statistics.

    So, a large a part of the engineering problem, and one price tackling in ahead-searching research, is to assemble bigger huge information solutions (reminiscent of capabilities) out of composable points to reduce the high can charge of engineering these solutions. beginning with the textile to manipulate substances, the fashion is pointing towards cloud oceans of containers-relocating from (digital) computer to method-level abstractions. Even at this degree, challenges abound if they need to map work run on behalf of distinctive tenants onto a single such ocean. (Container oceans are the natural substances to drain your facts lakes into!) On correct of such infrastructure, they ought to address the core challenges of affinitizing computations to the dominant 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 certain units of necessities. simply as with components, where they may have hoped for the definitive set of constructing blocks from which to compose all 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 turn up-just as it failed to occur for accessories.

    during this advanced space, they want research into improved the right way to manage resources (oceans) to handle contradictory requirements of collocation, consistency, and distribution. Abstractions of homogeneity destroy 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 work on behalf of possibly malicious or collectively adverse tenants requires deep 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 happen to depend on isolation at the digital-computing device-stack or hardware cluster tiers.

    nowadays, we're a little 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 need 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 also the a variety of proprietary applied sciences present in the general 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 catch 22 situation of option.

    altering How They engage with statistics Martin Petitclerc

    there are many technologies for big information engineering, and nobody know-how matches all wants. an important change exists between tuning a gadget for a specific dataset (repeating the equal jobs) and having a gadget that tunes itself on demand (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 also locate how you can cut back the human intervention integral to get 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 knowledge assistance grows. All knowledge items of suggestions aren't equally critical to every person, and their price may change over time. anything unimportant today may turn into critical 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 correct time.

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

    Involving cognitive systems in all phases of the facts manner is how to reduce human intervention. it be also a way to hyperlink the statistics to clients' initiatives, targets, and dreams, defining all 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 irrelevant or unimportant issues. as an example, consider 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 have an impact on analyses detailing how each of those moves would impact the outcome.

    Such techniques may still empower everyone to understand data with out them having to turn into a knowledge 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 relevant actions on the necessary facts at the required time. here is probably improved than having the IT folk put together a big 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 take place in a black-box manner, and facts lineage is vital in order that the clients can be aware what become carried out, why, and the way 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 work 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 all through the organization might then use this mannequin to function 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 way even though privateness turned into blanketed at distinct particular person access features. They might hyperlink geospatial and temporal records to different statistics and correlate all the facts to identify an entity (equivalent to someone).

    The analysis group should center of attention on simplifying the handling of records so that it's greater contextual and on demand, devoid of requiring IT intervention at all levels of the procedure. The community also need to check how cognitive programs can empower all types of users in an atmosphere by which the quantity, speed, and range 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 general contain performance, but empowering americans to straight away reap information is what is going to make organizations greater positive.

    coping with the Scaling Cliff Roger Barga

    massive data and scalability are two of the most well-liked and most important themes in present day speedy-transforming into facts analytics market. not most effective is the price 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 need be. but what do they really imply by means of scalability? A equipment 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 work however can additionally mean 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 run out of larger packing containers to purchase, and including supplies will fail to come back advancements-you're going to have run off the edge of the scaling cliff. Scaling cliffs are inevitable in huge statistics techniques.

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

    Asynchronous versus SynchronousTime is probably the most effective 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 cut the time a server is dedicated to processing a request. Servers can then queue long-operating operations for completion later by using a separate system or thread pool.

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

    dealing with Contentious ResourcesAll systems possess finite actual substances; competition for these supplies is the basis reason behind all 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 use a contentious resource except fully integral, but when you need to use 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 make sure that contentious elements are back to the pool inside a hard and fast time length. This design can start with quick SSL (comfortable Sockets Layer) termination at the load balancer. Hardware load balancers have crypto playing cards that can terminate SSL efficaciously in hardware and decrease the front-conclusion server load by using as a good deal as 40 p.c. The quick SSL termination will also boost client performance. you could follow this principle throughout the device layers.

    Logical PartitioningLogically partition components and activities all the way through the gadget, and minimize the relationships between them. Partitioning actions can assist ease the burden on high-cost supplies. A top-quality practice is to logically partition your application between the proxy or person interface layer, handle airplane layer, and records airplane layer. youngsters logical separation does not mandate actual separation, it permits actual separation, and you may scale your equipment 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 also 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 make certain your device is balanced, and not using a single layer providing a bottleneck or an overabundance of components, the latter of which may end up in underutilization or put pressure on different layers in the gadget.

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

    So, a ideal follow is to replicate state across servers within the equal logical layer. should still the server come under load and be a point of aid competition, different servers in the equal logical layer can proceed the session through the use of the state in their cache. youngsters, peer-to-peer gossip protocols can break down at massive scale, so a small (log N) dedicated caching fl eet is required. each server persists state to a single server within the caching fl eet, which then disseminates this throughout a quorum in the fleet. These servers can lazily propagate state to servers within the logical layer in an efficient and scalable method.

    Divide and ConquerAt some factor, all 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 solve 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 separate example of your system to achieve massive scale.

    automated ResiliencyThere are many open challenges on the road to more advanced and scalable big data programs. One problem that warrants further analysis is automatic resiliency. A smartly-designed big statistics device may also be resilient enough to resist the unexpected lack of one or more computing resources. however a very resilient gadget requires each first rate design and repair-stage guide to instantly become aware of and substitute situations that have failed or become unavailable. When a brand new illustration comes online, it'll take into account its function in the device, configure itself, find its dependencies, provoke state 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 development 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 satisfy the challenges of operating respectable, flexible agencies, IT managers and technical leads count 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 real 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 big data Application Performance Management (APM), and Cloudwick, leading provider of digital business services and solutions to the Global 1000, today announced a collaborative offering for enterprises migrating their big 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 move to ensure the same or better performance and SLAs.

    View photos

    "The biggest challenge for enterprises migrating big data to the cloud is ensuring SLAs are maintained without having to devote 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 business are unavoidable," said Mark Schreiber, General Manager for Cloudwick. "Without Pepperdata's metrics and analysis before and after the migration, there is no way 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 analyze performance and SLAs, accurately map workloads to appropriate AWS instances, and provide cost projections. Once the AWS migration is complete, the same operational metrics from the cloud are collected and analyzed to assess performance results and validate migration success.

    To learn more, stop 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 big 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 deep expertise to deliver predictable performance, empowered users, managed costs and managed growth for their big data investments, both on-premise and in the cloud. Leading companies like Comcast, Philips Wellcentive and NBC Universal depend on Pepperdata to deliver big 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 join NSF’s Big Data Program | killexams.com real 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 big data, Critical Techniques, Technologies and Methodologies for Advancing Foundations and Applications of Big 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, subject 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 form of cloud credits/resources for projects funded through this solicitation.

    This novel collaboration combines NSF’s experience 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 also builds upon the shared interests of NSF and the cloud providers to accelerate progress in research and innovation in big 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 real datasets; demonstration of the scalability of approaches; and development 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).  All those interested in submitting a proposal to the BIGDATA program should refer to the solicitation for details. All 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 use of cloud resources has been adequately justified through an annual usage plan; and (3) the estimate 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 also for usage over the duration of the project (e.g., software development and testing and code debugging).

    We are excited to offer this opportunity and look forward to the response of the national big 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 all fields of science and engineering. In fiscal year (FY) 2019, its budget is $8.1 billion. NSF funds reach all 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 News Updates by Email 

    Useful NSF Web Sites:NSF Home Page: https://www.nsf.govNSF News: https://www.nsf.gov/news/For the News 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 big data | killexams.com real 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 development 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 share more plant data with their workers while lowering their IT burden. Building on a base of more than 70,000 Wonderware Historian licenses, the company’s new Historian Online Edition offering can help 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 way 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 also improves usability for the end 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 standard 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, flexible 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 burden of requiring expensive IT infrastructure to bring new products quickly online,” said Dewey Forrester, senior director, business development and evangelism at Microsoft.

    Edited by Peter Welander, pwelander@cfemedia.com

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



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