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70-776 Performing mammoth Data Engineering with Microsoft Cloud Services

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Microsoft Performing mammoth Data Engineering

Analyze Elasticsearch statistics in R | killexams.com true Questions and Pass4sure dumps

Java-based mostly (JDBC) information connectivity to SaaS, NoSQL, and massive statistics. down load Now.

that you may entry Elasticsearch data with absolute R script and common SQL on any computing device the position R and Java can likewise breathe establish in. which you could exercise the CData JDBC Driver for Elasticsearch and the RJDBC kit to labor with faraway Elasticsearch statistics in R. by using the CData Driver, you're leveraging a driver written for industry-confirmed requirements to entry your statistics in the popular, open-source R language. this text suggests the passage to exercise the driver to execute SQL queries to Elasticsearch and visualize Elasticsearch data via calling ordinary R capabilities.

install R

which you can match the driving force's performance beneficial properties from multithreading and managed code by using running the multithreaded Microsoft R Open or by operating open R linked with the BLAS/LAPACK libraries. this text uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This photo ensures reproducibility.

Load the RJDBC package

to invent exercise of the driver, down load the RJDBC package. After setting up the RJDBC kit, prerogative here line masses the package:

library(RJDBC) connect to Elasticsearch as a JDBC records supply

you'll exigency prerogative here counsel to hook up with Elasticsearch as a JDBC statistics supply:

  • Driver class: Set this to cdata.jdbc.elasticsearch.ElasticsearchDriver.
  • Classpath: Set this to the location of the motive oblige JAR. with the aid of default, here's the lib subfolder of the installation folder.
  • The DBI capabilities, similar to dbConnect anddbSendQuery , deliver a unified interface for writing statistics access code in R. exercise prerogative here line to initialize a DBI driver that can invent JDBC requests to the CData JDBC Driver for Elasticsearch:

    driver <- JDBC(driverClass = "cdata.jdbc.elasticsearch.ElasticsearchDriver", classPath = "MyInstallationDir\lib\cdata.jdbc.elasticsearch.jar", identifier.quote = "'")

    that you would breathe able to now exercise DBI features to hook up with Elasticsearch and execute SQL queries. Initialize the JDBC reference to the dbConnect function. beneath is a typical JDBC connection string:

    conn <- dbConnect(driver,"Server=127.0.0.1;Port=9200;consumer=admin;Password=123456;")

    Set the Server and Port connection properties to connect. To authenticate, set the person and Password residences, PKI (public key infrastructure) homes, or both. to exercise PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.

    The information provider makes exercise of X-Pack protection for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. word: TLS/SSL and customer authentication exigency to breathe enabled on X-Pack to exercise PKI.

    once the statistics provider is related, X-Pack will then role user authentication and provide role permissions based on the nation-states you've got configured.

    Schema Discovery

    the driver fashions Elasticsearch APIs as relational tables, views, and saved approaches. exercise prerogative here line to retrieve the listing of tables:

    dbListTables(conn) Execute SQL Queries

    which you can exercise the dbGetQuery function to execute any SQL query supported by means of the Elasticsearch API:

    orders <- dbGetQuery(conn,"opt for Orders.Freight, consumers.ContactName FROM valued clientele inner breathe a section of Orders ON valued clientele.CustomerId=Orders.CustomerId")

    which you can view the outcomes in a lore viewer window with the following command:

    View(orders) Plot Elasticsearch facts

    that you would breathe able to now anatomize Elasticsearch information with any of the statistics visualization programs attainable within the CRAN repository. that you may create primary bar plots with the constructed-in bar plot characteristic:

    par(las=2,ps=10,mar=c(5,15,4,2)) barplot(orders$Freight, leading="Elasticsearch Orders", names.arg = orders$OrderName, horiz=genuine)

    issues:

    big records ,elasticsearch ,r ,records evaluation ,tutorial


    HireVue Appoints Microsoft AI executive Eric Boyd to Board of directors | killexams.com true Questions and Pass4sure dumps

    SALT LAKE city, Oct. 8, 2018 /PRNewswire/ -- HireVue, issuer of the most complete AI-pushed talent evaluation suite and video interviewing solutions, has named Eric Boyd, CVP AI Platform at Microsoft,  to the HireVue Board of administrators.

    "i'm extremely joyful to welcome Eric to the HireVue Board of administrators," stated Kevin Parker, Chairman and CEO at HireVue. "His wealth of adventure and wonderful viewpoint in AI and analytics may breathe of large value as they proceed to accelerate HireVue's know-how for their world valued clientele."

    Boyd has spent pretty much a decade at Microsoft main gigantic-scale efforts such because the Bing advertisements engineering group, together with the relevance AI, excessive-efficiency serving methods, and large-facts analytics systems. In his latest role as CVP AI Platform, he oversees structure of the AI equipment, infrastructure, hardware, mammoth information systems and key data units powering the computing device-getting to know techniques using Bing, Bing advertisements, and Microsoft office.

    past in his career, he served as vice chairman of Engineering for Mochi Media and spent over 9 years in engineering leadership roles at Yahoo!, the position he was finally promoted to vice chairman of Platform Engineering. He holds a B.S. in laptop Engineering and mathematics from the Massachusetts Institute of expertise.

    "it breathe striking to breathe section of the HireVue board at a time of such large boom," Boyd talked about. "Few organizations carry on a vision the manner that HireVue has delivered on the thought of democratizing the recruiting system. With such a robust Board in region, i am looking forward to helping e engage HireVue to the next degree as a corporation."

    Boyd's appointment follows a 12 months of vast boom:

  • In may additionally, HireVue received London-based MindX, a online game-based assessments company, to build out probably the most potent AI-driven pre-appoint assessments platform available on the market.
  • The traffic is on track to boost modern traffic via nearly 35 percent year over 12 months.
  • HireVue has hosted over seven million video interviews so far, up from the business's milestone of five million interviews executed in October 2017.
  • overseas earnings indulge in accelerated more than 50 p.c yr over 12 months and now contain very nearly a fifth of complete revenue bookings.
  • The variety of HireVue personnel grew with the aid of virtually 50 p.c in two years to over 300 employees, 90 of whom are committed to product construction and facts security and privacy.
  • About HireVueAcross the globe, HireVue is reworking the style businesses find, employ and enhance the choicest ability with Hiring Intelligence. using a combination of validated industrial/organizational science and fastidiously demonstrated, predictive artificial intelligence, recruiting consultants are hiring greater nice ability, quicker. HireVue is available worldwide in additional than 30 languages and has hosted basically eight million interviews for greater than seven-hundred valued clientele worldwide, together with over one-third of the Fortune a hundred and leading manufacturers reminiscent of Vodafone, Intel, Hilton, HealthSouth, Qantas and Carnival Cruise strains. For greater tips, visit www.hirevue.com.

    HireVue companionable NetworksTwitter:  www.twitter.com/HireVue LinkedIn:  www.linkedin.com/enterprise/HireVue  fb: www.fb.com/HireVue   YouTube: www.YouTube.com/user/HireVue Instagram:  www.instagram.com/hirevue/

     

    Cision View original content:https://www.prnewswire.com/information-releases/hirevue-appoints-microsoft-ai-government-eric-boyd-to-board-of-administrators-300725516.html

    source HireVue


    Microsoft is hiring former Qualcomm engineers to labor on its quantum computing crew | killexams.com true Questions and Pass4sure dumps

    beginning in June, Microsoft started hiring a couple of former Qualcomm engineers and bosses who at the flash are engaged on Redmond's quantum-computing crew. a couple of these engineers had been formerly working on Qualcomm's ARM-based mostly server efforts, as first said on October eight through The information.

    msquantumqualcomm.jpg credit score: Microsoft

    during the terminal couple of years, Microsoft publicly has talked up its intentions to breathe a player in quantum computing, however officers dispute the company has been working on scalable quantum computing for very nearly two decades. Microsoft officials dispute they map to exercise a quantum desktop as a co-processor, with builders deploying their quantum applications from within Microsoft Azure.

    Quantum computers are designed to process in parallel, as a consequence enabling modern sorts of applications throughout loads of workloads. they're designed to harness the physics of subatomic particles to provide a special technique to store information and solve complications in comparison to universal computers. The influence is that quantum computers, developed around custom quantum processors, are anticipated to breathe capable of resolve determined excessive-performance-computing complications more correctly.

    The suggestions referred to names of a couple of former Qualcomm personnel who grew to breathe Microsoft personnel this summer. I discovered up-to-date LinkedIn profiles for 4 of the individuals they cited:

  • Muntaquim Chowdhury became vp of Tech at Qualcomm in Raleigh-Durham, N.C., and worked as chief architect of Server CPU at Qualcomm from February 2016 to July 2018. (He turned into at Intel for 24 years earlier than that.) He joined Microsoft (in Bellevue, Wash.) as a associate Architect.
  • Thomas Speier was a 15-year Qualcomm worker. He turned into Senior Director at Qualcomm and an architect for customized ARM cores. In June he grew to become fundamental Architect at Microsoft in Raleigh-Durham.
  • Michael McIlvaine turned into Senior Director of Tech, and a faultfinding CPU architect at Qualcomm, where he worked for just about 15 years, including a stint engaged on "subsequent gen server CPU." he's now a primary architect with Microsoft in Raleigh-Durham.
  • Michael Underkoffler, who joined Microsoft in June, is a colleague Engineering manager in Raleigh-Durham. His profile says he's "building a control rig for quantum computing it truly is based on a classical laptop architecture." earlier than that, he become a Senior Director of Engineering at Qualcomm in Raleigh Durham, where he worked for 14 years on ARM processor design, among other jobs.
  • In July 2018, Microsoft appointed former Microsoft unusual Engineer and researcher Doug Burger because the head of its Azure Hardware Engineering crew. (Burger became a Microsoft Technical Fellow on the equal time.) Burger's mission, I've heard, is to spearhead a few different, non-consumer-focused hardware efforts across the enterprise, including Microsoft's field-programmable gate-array work, its quantum computing efforts, modern datacenter labor affection its "challenge Natick" underwater datacenter and greater. Org-chart-intelligent, Burger studies to Todd Holmdahl, who is company vice president of Quantum Computing.

    Microsoft officers firstly referred to they'd speak greater concerning the business's growth on quantum computing at the recent Ignite IT professional convention, however ended up asserting very shrimp at that event about quantum. possibly Microsoft moved its update on quantum to a different of its activities later this year (?)


    70-776 Performing mammoth Data Engineering with Microsoft Cloud Services

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

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    Vendor denomination : Microsoft
    free pdf : 69 true Questions

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    Google provides modern options for cloud data storage locations and access | killexams.com true questions and Pass4sure dumps

    In one of several additions to its cloud computing offerings today, Google LLC announced modern ways invent data more accessible in its Cloud Storage service and control where it’s stored around the world.

    Introduced early Thursday at the company’s Cloud Next conference in London, the modern options for storage across multiple regions are intended to assist customers for applications such as content delivery and big-data workloads.

    For one, Google announced a dual-regional option in beta test mode, allowing customers to store data in a pair of specific Google Cloud regions. Often with many cloud storage services, Google Cloud product manager Geoffrey Noer said in an interview, users indulge in to create multiple locations for their data and then exercise replication tools to manage the synchronization of that data.

    Google’s modern option, by contrast, provides the availability and backup of geographical replication of storage with low-latency storage access. “You write just one copy and they accomplish the rest,” Noer said. “In standard enterprise offerings, if you lose your primary site, you exigency to manage getting your data up again.”

    The option is especially useful for analytics or big-data workloads, for which having data and computing proximate together provides faster performance, and for serving content such as video.

    For example, Twitter Inc. plans to exercise the dual-region option for its data platform, in particular for one large log analysis project the microblogging site just completed. Derek Lyon, Twitter’s director of engineering, will profile at Cloud Next this week how it’s affecting 300 petabytes of data to Google’s cloud, but for this particular project it had to sprint queries on a massive 15 petabytes of compressed log files. That was going to breathe difficult ingesting data from only one region.

    “We chose dual-region buckets because they provide us with the ingest performance and multiregional availability of multiregion buckets, combined with the data locality of regional buckets,” he said.

    The dual-regional option is an addition to current options for regional — data stored in a specific region with replication across that region’s availability zones — and multi-regional, where data is distributed across the U.S., the E.U. or Asia (below).

    gcpregions

    Google likewise introduced higher availability for so-called nearline data, which is data that needs to breathe overwritten, say, once a month, and coldline data, for “cold” storage of data that may not change more than once a year. Both are lower-cost archival options than “hot” storage of oft-accessed or overwritten data.

    Now, both options are stored redundantly in multiregional locations so they’re more available, with millisecond latency. “Most data has a lifecycle where its relevance (and subsequently its access frequency) is highest immediately after it’s been created, and then tails off as it ages,” Noer explained in a blog post written with product manager Ben Chong and Chris Talbott, head of Cloud Storage product marketing. “We believe that as content is accessed less frequently, you should profit from lower-tier storage costs while maintaining a consistent customer suffer and data availability and reliability.”

    The modern services, along with user interface and other tweaks coming soon, are section of a continuing shove by Google to become more of a leader in the cloud computing business.

    One analyst celebrated that Google is playing catch-up generally to larger rivals Amazon Web Services Inc. and Microsoft Azure. “But it’s the automation and ease of exercise that determines the uptake,” said Holger Mueller, vice president and principal analyst at Constellation Research Inc.

    “Google is making ample progress on its storage portfolio, and cloud storage matters for enterprises as they build their next-generation applications in the cloud,” he added. “So redundancy, performance and resilience capabilities affection the ones that Google is adding are key for CxOs to reckon the cloud as storage for their next-gen applications.”

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    SAP's road to cloud-native data platforms in a hybrid world | killexams.com true questions and Pass4sure dumps

    At SAP's 2018 TechEd conference that has just wrapped up Las Vegas, it made several announcements for common availability and betas of SAP HANA and data integration products with an eye toward the cloud. SAP has offered HANA in the cloud for some time; for instance, you can depart to AWS Marketplace and order up an instance, taking your existing license.

    SAP likewise announced common availability of SAP Data Hub 2.3, which was section of their SAPPHIRE coverage back in the spring. To recap, Data Hub manages data pipelines, providing the flexibility to walk data and/or shove down compute to the data source. The notion with data pipelines is chaining together operations related to sourcing, ingesting, cleaning, integrating, and in some cases, executing ML processes and performing light in-line analytic and/or filtering operations on data. Version 2.3 takes faultfinding steps toward unifying metadata management and data exploration.

    SAP is designing Data Hub with an eye on cloud deployment, advocate of multi-cloud data landscapes, and on managed cloud service delivery. For now, it is a bring your own license service - not yet a managed service. But there is precedent with SAP Data Network, providing a managed service for data discovery, enrichment, and analytics. The plot thickens with the announcement at TechEd terminal week of the beta for SAP HANA database as a service. affection Data Hub, you could already sprint HANA in the cloud, but it was targeted at boost and shift where you managed HANA in the cloud rather than your own data center.

    There's shrimp shock or awe about SAP's HANA as a service announcement. (Postscript: This was announced at SAPPHIRE terminal spring.) It's more an inevitability where SAP's rivals are already running there. We've been mammoth proponents of managed cloud database services because, unlike infrastructure as a service (which is the modern, multi-tenanted equivalent of hosting), managed cloud services fully deliver on the covenant of simplification. Set the instance, size of the footprint, and service flat that you want, and leave the messy, non-value-added jobs of provisioning, patching, and maintenance to the database provider. And by the way, the selfsame goes with any configuration of packaged software; they can bar not a bit thank Salesforce.com for raining awareness of the benefits of SaaS (and PaaS) services when it comes to allowing IT and the traffic to focus on more value-added tasks.

    Of course, the rub with managed cloud services is the lock-in factor, but that's where cloud challengers affection SAP compass in: depending on where they invent their managed service available, they are not necessarily locking you into the usual suspects: AWS, Azure, or Google Cloud.

    But that's where the balancing act comes in. If you're Amazon or Google, it is much easier to provide a consistent suffer to your managed database service, whether it breathe Aurora, Cloud Spanner, DynamoDB, or Cloud Firestore, because it's only delivered on their environment.

    For incumbents affection SAP (and Microsoft, for that matter), who indulge in the on-premise legacy, there is the challenge and opening of giving customers the best of both worlds. accommodate your on-premise product to give a consistent suffer with the cloud counterpart, and you indulge in a powerful wedge against cloud-only providers, not to mention a ready concede for those demanding private cloud deployments. This is an locality where IBM, Oracle, and Microsoft are staking preemptive claim.

    But to deliver that common experience, the devil is in the details. At the base, most cloud-native platforms divide compute from storage to provide elasticity and ease of scaling. At first blush, that sounds affection a nice scheme for on-premise, except that data hub deployments won't indulge in the virtually illimitable scalability and multi-availability zone replication for HA/DR (and improved local performance for reads and, for multi-master databases, writes). As they pointed out terminal week, Microsoft is rise to perform this balancing act with SQL Server 2019 and Azure SQL Database.

    The flip side of cloud endemic is containerization and microservices. By deconstructing monolithic on-premise databases and applications, they can utilize cloud resources far more efficiently. If you are running a change data capture log, for instance, you don't indulge in to fire up the total database engine and therefore reduce the amount of compute footprint to consume.

    Containerization is not an all-or-nothing deal. You can establish the entire database in a container, affection SAP has already done with SAP Data Hub which includes SAP Vora (which allows you to easily deploy on different clouds seamlessly using Kubernetes), or you can depart further and deconstruct the platform into multiple containers that in the long sprint could really exploit the efficiencies and scale of cloud. For now, SAP has taken the first option, but it's still a key step forward in promoting its hybrid, cloud-agnostic odds and in making HANA ready to breathe deployed as a container on Data Hub.

    Now we're waiting for the other shoe to drop. SAP Data Hub is a product that's begging for cloud deployment because so much of the data that SAP customers will breathe working with will reside in the cloud. SAP has not announced any plans yet, but watch this space. And indirectly related to this is how SAP supports data scientists performing advanced analytics, modeling and machine learning. Given SAP's all-in advocate for Leonardo, its venture to develop industry expertise in AI, and the embedding of the fruits of those labors in SAP enterprise applications, they are expecting that SAP will plant its stake there soon. And yes, a lot of that (especially IoT) will require running in the cloud. So, we're looking for SAP to accomplish two things: bag the wheels in motion for a cloud-native data science platform that in spin will reuse much of the data engineering capabilities of Data Hub.


    Cloud buoys data microservices -- for on-premises systems, too | killexams.com true questions and Pass4sure dumps

    It shouldn't breathe too startling to learn that cloud architectures are coming to data centers. Even if they are stuck on an organization's premises, today's enterprise architects want to bag in on the action as data microservices evolve.

    The cloud architectures they are bringing down to earth compass largely via containers that are based on a microservices approach akin to old-time SOA -- which stands for service-oriented architecture, not Sons of Anarchy, the outlaw biker club from the TV point to of the selfsame name. Data microservices are affection SOA, only more micro.

    Improving mammoth data advocate for containers was one of the engineering to-do list items cited by Hadoop vendors Cloudera and Hortonworks when they announced plans to merge this month. That and a walk to wider exercise of cloud expostulate storage depict a mammoth shift coming to mammoth data.

    Kubernetes orchestration

    While containers were born as stateless vessels, labor is underway to invent them more stateful, with advocate for persistence of data in applications. If successful, this could simplify the exercise of multiple for-purpose analytics engines and frameworks -- deem Hive, Presto, Druid, TensorFlow and so on.

    Used together with the increasingly celebrated Kubernetes container orchestration technology, data microservices will soon emerge as an alternative style of data processing, whether on the cloud or off.

    It's early, and there is a lot of learning to do, as evidenced by Kubernetes sessions that were often full at terminal month's Strata Data Conference in modern York. After learning comes building, much of which is necessary before the modern architecture can buy root.

    Enter Cloud Private for Data

    For IBM and its customers, finding a poise between on-premises and cloud data architectures is an ongoing quest. The company's vigorous embrace of both Hadoop and Spark has been celebrated before, but IBM is still working to neatly package those and other mammoth data mainstays for cloud and on-premises deployments.

    Near the wait of the Strata conference, they spoke with Rob Thomas, common manager of IBM Analytics, as he prepared for a panel discussion on the company's IBM Cloud Private for Data platform and the role it can play in bringing AI to fruition. The Cloud Private for Data package includes Db2, Db2 Warehouse and Db2 Event Store, and it supports various interfaces for data scientists and other predictive analytics users, both on and off the cloud.

    "If you built a model on private cloud, you can deploy it on public cloud. Or, if you built it on public cloud, you can deploy it on private cloud," Thomas said. The data preparation and governance tools are the selfsame whether the deployment is public or private, he added.

    Thomas said the company recently followed through on plans to add advocate for MongoDB, EnterpriseDB's EDB Postgres and IBM Data Risk Manager to IBM Cloud Private for Data, and it is working to certify the platform to labor with Red Hat's OpenShift container application technology.

    Chart showing differences between monolithic and microservices Monolithic vs. microservices

    IBM's traditional data-side competitors are likewise looking to connect data analytics on premises with data analytics in the cloud. Oracle's efforts hub on its Oracle Cloud at Customer undertaking. And Microsoft recently previewed a version of SQL Server 2019 that can bring the Azure versions of Spark and Hadoop down into the data center.

    IBM has significant cloud evolution and data alliances with Red Hat and Hortonworks, both of which feel on data microservices. Both companies combined with IBM just before Strata to launch an Open Hybrid Architecture Initiative. The project's purpose is to meld Kubernetes, containers and Hadoop workloads, and there is much labor to breathe done in that regard, Thomas said.

    Remaking the monolithic

    After Strata, they checked in with Tom Phelan, co-founder and chief architect at BlueData Software, maker of the software platform BlueData Epic, which is short for Elastic Private Instant Clusters. He said he liked the goals of the Open Hybrid Architecture Initiative, but added that there must breathe more labor before it can walk forward.

    "A mammoth issue is that, when Hadoop was written, it was written as a monolithic architecture," Phelan said.

    That is the case even though Hadoop can breathe broken down into services such as the denomination node, the YARN resource manager, Hive, Hadoop Distributed File System services and so on.

    When Hadoop was written, it was written as a monolithic architecture. Tom Phelanchief architect, BlueData

    "None of those are microservice-architected," Phelan said. "Those services indulge in to breathe started in a inescapable order. They indulge in to interact with each other service in a very particular way. There is, for example, no passage to restart one of those services and not indulge in it repercussion other services that are running."

    For its part, BlueData this month released KubeDirector, an open source project for developing stateful application clusters on Kubernetes.

    New scheme coming?

    Whether data moves to the cloud or not, the influence of cloud architecture looms as substantial.

    "Companies obviously indulge in a lot of on-premises data going into the cloud, but not bar not a bit the data is going to the cloud," said Forrester analyst Noel Yuhanna. "Sometimes, when the cloud bill comes in, it may breathe huge -- maybe more than on premises."

    Moves such as the Open Hybrid Architecture Initiative address the exigency to harmonize views in the two domains, according to Yuhanna, and set the stage for common management policies for both.

    "You want an architecture that can manage both cloud and on-premises data with common policies," he added.

    Back when SOA walked the earth, it brought about a reworking -- or rewrapping -- of legacy systems. modern data microservices could invent existing mammoth data systems akin to legacy systems. That would breathe nothing more than progress.

    It's a ample bet that, as hybrid data processing becomes more real, data will breathe shuttling from here to there and back. And, of course, YANA -- Yet Another modern Architecture -- will breathe waiting in the wings.



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