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70-776 | Performing Big Data Engineering with Microsoft Cloud Services

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70-776 - Performing Big Data Engineering with Microsoft Cloud Services - braindump

Vendor Microsoft
Exam Number 70-776
Exam Name Performing Big Data Engineering with Microsoft Cloud Services
Questions 69 Q & A
Recent Update January 15, 2019
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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
Q&A : 69 Real Questions

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Microsoft Performing Big Data Engineering

Panoply Is a Finalist In Microsoft AI and massive statistics competitors | killexams.com Real Questions and Pass4sure dumps

Panoply will compete in Seattle in the international competition

SAN FRANCISCO, Dec. 11, 2018 /PRNewswire/ -- Panoply, the smart cloud information warehouse built for enterprise intelligence, is happy to announce its fame as a finalist in Microsoft and Calcalist's artificial Intelligence and big records Startup competitors.

Panoply brand (PRNewsfoto/Panoply)

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In a stiff competitors, Microsoft will host representatives from Panoply and the other finalists at its Seattle headquarters. the first and 2d area winners may be invited with the aid of Calcalist to participate in the newspaper's Berlin conference in 2019, where the organizations will meet with talents companions and traders.

Panoply's CEO and co-Founder Yaniv Leven talked about, "We're proud to be announced as a finalist in the artificial Intelligence and big data Startup competitors. so far, we've been amazed on the first-rate of the corporations collaborating and we're psyched to be pitching at the finals. We're a team of engineers and fighters, we set lofty dreams and conquer big challenges to prove how dangerous our ingenious desire is."

last 12 months's competitors winner became Clew scientific Ltd., which makes use of AI applied sciences to aid hospitals reduce remedy time and costs through tracking patient facts in real time and monitoring the means of healthcare providers.

About PanoplyPanoply is a wise data stack that makes use of computer discovering and herbal language processing to automate all 3 key points of the information stack: data collection & transformation (ETL), database storage administration, and query efficiency optimization. Panoply empowers any one working with information analytics to without delay profit actionable insights on their personal - devoid of the want of IT and Engineering. Panoply is based in San Francisco and Tel Aviv, is privately held and funded via traders reminiscent of Intel Capital, 500 Startups, Blumberg Capital, and C5 Capital

 

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10. What's Up with Microsoft office?Microsoft's selection for the subscription-based workplace 365 over the "perpetual license" edition is beginning to exhibit up in its characteristic descriptions. read right here.

9. How Will windows Server 2019 have an effect on Hyper-V architecture?Microsoft's approaching home windows Server product appears to be choked with new points. listed here are one of the most standouts, specific to Hyper-V. examine right here.

8. Spectre and Meltdown: How Do They have an effect on SQL Server?IT needs to patch everything, but the Spectre/Meltdown patches come with their own complications, too. right here's how to limit the fallout. study right here.

7. the way to Configure a File background Backup for home windows 10As far as set-it-and-neglect-it backup options go, there are few greater handy than the File heritage function it's built natively into home windows 10. read here.

6. how to Create a home windows 10 equipment photograph BackupMicrosoft offers you the skill to create a complete backup of your whole laptop in windows 10. You simply have to do a bit digging to locate it. read right here.

5. how to enhance the performance of windows 10 in a Hyper-V VMBrien walks during the steps of troubleshooting a (very) slow windows 10 virtual computing device. hint: Disable your virtual memory at your own chance. examine right here.

four. SQL Server 2019's big records Clusters ExplainedThe biggest feature within the SQL Server 2019 preview launched at Ignite is SQL Server big facts clusters. Travis Wright, Microsoft's foremost software manager for SQL Server, explains exactly what this potential for administrators. read here.

3. Introducing SQL Server 2019: What's New within the Public PreviewMicrosoft introduced the newest preview of its subsequent-gen SQL Server product at Ignite. right here's a rundown of every little thing the brand new release guarantees, from performance enhancements to safety and availability perks. examine here.

2. how to function a USB Passthrough in Hyper-VIf you are customary with how to entry a USB equipment from internal a VMware ambiance, be mindful -- the method is rarely fairly as seamless as it is in Hyper-V. here's the way to do it in Hyper-V on the host and client stages. examine right here.

1. office 2019: Meet the brand new workplace, equal because the ancient OfficeWhile testing out the workplace 2019 preview, Brien noticed whatever unexpected: office 2019 appears essentially indistinguishable from workplace 2016. examine right here.


Microsoft BCP efficiency on Sqoop EXPORT to SQL Server From Hadoop | killexams.com Real Questions and Pass4sure dumps

The open supply HPCC techniques platform is a confirmed, convenient to make use of answer for managing information at scale. seek advice from our convenient e-book to be trained more about this fully free platform, check pressure some code within the on-line Playground, and get all started today.

we have gotten everybody connected to SQL Server using growth DataDirect's exclusive guide for each NTLM and Kerberos authentication from Linux with Sqoop. Now, we plan to blow your minds with excessive-flying bulk insert performance into SQL Server the usage of Sqoop's frequent JDBC Connector. Linux clients will get equivalent throughput to the Microsoft BCP device.

sqoop-logo

thus far, Cloudera and HortonWorks had been pointing retail outlets to the high-performance DataDirect SQL Server JDBC driver to assist load records volumes any place from 10GB to 1TB into SQL Server information marts and warehouses. it be usual for the DataDirect SQL Server JDBC driver to pace up load instances via 15-20X, and Sqoop will see identical development due to the fact that it leverages JDBC batches that we transparently convert into SQL Server's native bulk load protocol. relocating records out of Hadoop and into exterior JDBC sources are enjoyable tasks that characterize the democratization of huge information for downstream application patrons. you're definitely doing anything appropriate if you are ready to examine on!

Get all started With fast efficiency for Sqoop EXPORT to SQL Server
  • download the DataDirect connect for JDBC drivers and observe the brief-birth courses provided with the down load.
  • copy the sqlserver.jar file to the $SQOOP_HOME/lib directory to your customer laptop. (This may be /consumer/lib/sqoop/lib if you installed from an RPM or Debian equipment). The JDBC driver must be installed simplest on the machine where Sqoop is carried out; and never on each and every node to your Hadoop cluster.
  • determine the database’s recuperation mode per the MSDN article on issues for Switching From the entire or Bulk-Logged recuperation mannequin. To examine the restoration mode, the database user can run right here question: choose identify, recovery_model_desc FROM sys.databases the place identify = ‘database_name’ ; word the recovery_model_desc again by means of this question (expect to come, 'BULK_LOGGED').
  • From the command line, run the Sqoop export command the usage of an identical homes as beneath. Or specify the equal using the Hue net UI for Sqoop jobs.
  • sqoop export --join 'jdbc:datadirect:sqlserver://nc-sqlserver:1433;database=check;user=test01;password=test01;EnableBulkLoad=proper;BulkLoadBatchSize=1024;BulkLoadOptions=0' --driver com.ddtek.jdbc.sqlserver.SQLServerDriver --desk 'blah_1024MB' --export-dir /user/hdfs/blah_1024MB/ --input-strains-terminated-through "n" --enter-fields-terminated-by means of ',' --batch -m 10 Notes
  • --batch mode is used for underlying insert commentary execution.
  • --driver must be distinct when the use of a widely wide-spread JDBC connector.
  • --join is the JDBC URL. EnableBulkLoad=actual authorizes the DataDirect SQL Server driver to make the most of the bulk load protocol for the inserting of rows. The BulkLoadBatchSize value indicates to the motive force the number of rows it'll try to bulk load on a single roundtrip to the server. If this value is below the sqoop.export.statistics.per.remark price, then every name to executeBatch will result in more than one round go back and forth to the server in an effort to insert the batch of rows.
  • --table: The table to be populated in the goal relational database as records is transferred from HDFS.
  • --export-dir identifies the HDFS directory that includes the Hadoop table to be exported.
  • --input-strains-terminated-with the aid of identifies the character which separates rows within the HDFS files.
  • --enter-fields-terminated-by way of identifies the character which separates columns within the HDFS information.
  • -D sqoop.export.data.per.statement isn't recommended nor the equivalent of JDBC batch size. rather, it specifies the variety of rows per SQL statements for statistics sources that help multi-row inserts similar to Postgres.
  • INSERT INTO films (code, title, did, date_prod, type) VALUES ('B6717', 'Tampopo', one hundred ten, '1985-02-10', 'Comedy'), ('HG120', 'The Dinner video game', one hundred forty, DEFAULT, 'Comedy');

    View the Sqoop person's e-book for complete reference.

    special because of Mike Spinak, major application Engineer, and Danh Huynh, systems Administrator, for their help with setup and checking out in the six-node Cloudera CDH5.2.0-1.cdh5.2.0.p0.36 cluster to export records into SQL Server 2008 R2.

    exhibit Me the Numbers

    outcomes are nevertheless coming in from a couple of shops and the most efficient to date is a load of forty GB into SQL Server within ten minutes. within the above gadget, we had been loading 37 GB in 18.5 minutes. There are a few residences throughout Hadoop, Sqoop, JDBC driver and SQL Server you could tune to enhance performance even further.

    Managing information at scale doesn’t should be challenging. learn the way the fully free, open source HPCC techniques platform makes it less complicated to update, easier to software, easier to integrate facts, and easier to manipulate clusters. download and get begun these days.

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

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

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    NSF and Internet2 to explore cloud computing | killexams.com real questions and Pass4sure dumps

    IMAGE: Conceptual design for new project Exploring Clouds for Acceleration of Science designed to use cloud computing to process near-real-time streaming data. view more 

    Credit: Moon Light PhotoStudio

    The National Science Foundation (NSF) has announced a new cooperative agreement with Internet2, a nonprofit computer networking consortium, to build partnerships with commercial cloud computing providers and support science applications in new and more effective uses of cloud computing capabilities.

    The NSF-funded Internet2 project, Exploring Clouds for Acceleration of Science (E-CAS), will investigate the viability of commercial clouds as an option for leading-edge research computing and computational science supporting a range of academic disciplines. Amazon Web Services and Google Cloud Platform have signed on as the initial cloud computing providers in this endeavor.

    "The E-CAS project has the potential to not only demonstrate the effectiveness of current commercial cloud computing services in supporting a range of applications that are important to the science and engineering research communities, but also to enable these communities to leverage the innovative technologies and capabilities to significantly accelerate scientific discoveries," said Manish Parashar, director of NSF's Office of Advanced Cyberinfrastructure.

    NSF has pledged $3 million for a two-phased approach to be managed by Internet2 and is expected to produce a deeper understanding of the use of cloud computing in accelerating scientific discoveries.

    "Internet2 looks forward to supporting NSF's vision as part of our ongoing commitment to working with the research and education community and cloud computing providers to accelerate time-to-science, and to facilitate discovery and sharing of ideas," said Internet2 President and CEO Howard Pfeffer. "This partnership will enhance a broad range of science and facilitate more effective integration of cloud computing resources and cyberinfrastructure for leading-edge research. Internet2 is delighted that we can participate in this activity on behalf of our members and the broader research and education community."

    The cooperation with Internet2 on E-CAS represents one of several recent investments made by NSF in collaboration with cloud computing providers, including Amazon Web Services, Google Cloud Platform, IBM Cloud and Microsoft Azure. For example, these services committed up to $15 million in cloud computing resources for projects funded by NSF's Critical Techniques, Technologies, and Methodologies for Advancing Foundations and Applications of Big Data Sciences and Engineering (BIGDATA) and Big Data Regional Innovation Hubs and Spokes (BD Hubs and Spokes) programs over a three-year period that began in 2017.

    E-CAS Next Steps

    The E-CAS project will invite proposals from researchers across the full range of science and engineering domains who have traditionally relied on high-performance computing resources and are interested in leveraging the novel capabilities of cloud computing platforms for their research activities. Proposals funded through the E-CAS project will be evaluated on two primary criteria:

    Acceleration of Science. The goal is to achieve the best time-to-solution for scientific applications and workflows using cloud computing.

    Innovation. A key measure of innovation will be the novel use of heterogeneous hardware resources and platforms, such as Central Processing Units, Graphics Processing Units and Field-Programmable Gate Arrays to support and extend scientific applications and workflows.

    E-CAS will have two phases. The first phase will support six different scientific and engineering applications and workflows with cloud computing allocations and resources for development and code migration. After identifying two final projects selected from the original six, the second phase will commence for another year with a focus on delivering scientific results.

    About Internet2®

    Internet2® is a non-profit, member-driven advanced technology community whose core infrastructure components include the nation's largest and fastest research and education network that was built to deliver advanced,customized services that are accessed and secured by the community-developed trust and identity framework.

    ###

    Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.


    Big data in the cloud | killexams.com real questions and Pass4sure dumps

    Big data and cloud technology go hand-in-hand. Big data needs clusters of servers for processing, which clouds can readily provide. So goes the marketing message, but what does that look like in reality? Both “cloud” and “big data” have broad definitions, obscured by considerable hype. This article breaks down the landscape as simply as possible, highlighting what’s practical, and what’s to come.

    IaaS and private clouds

    What is often called “cloud” amounts to virtualized servers: computing resource that presents itself as a regular server, rentable per consumption. This is generally called infrastructure as a service (IaaS), and is offered by platforms such as Rackspace Cloud or Amazon EC2. You buy time on these services, and install and configure your own software, such as a Hadoop cluster or NoSQL database. Most of the solutions I described in my Big Data Market Survey can be deployed on IaaS services.

    Using IaaS clouds doesn’t mean you must handle all deployment manually: good news for the clusters of machines big data requires. You can use orchestration frameworks, which handle the management of resources, and automated infrastructure tools, which handle server installation and configuration. RightScale offers a commercial multi-cloud management platform that mitigates some of the problems of managing servers in the cloud.

    Frameworks such as OpenStack and Eucalyptus aim to present a uniform interface to both private data centers and the public cloud. Attracting a strong flow of cross industry support, OpenStack currently addresses computing resource (akin to Amazon’s EC2) and storage (parallels Amazon S3).

    The race is on to make private clouds and IaaS services more usable: over the next two years using clouds should become much more straightforward as vendors adopt the nascent standards. There’ll be a uniform interface, whether you’re using public or private cloud facilities, or a hybrid of the two.

    Particular to big data, several configuration tools already target Hadoop explicitly: among them Dell’s Crowbar, which aims to make deploying and configuring clusters simple, and Apache Whirr, which is specialized for running Hadoop services and other clustered data processing systems.

    Today, using IaaS gives you a broad choice of cloud supplier, the option of using a private cloud, and complete control: but you’ll be responsible for deploying, managing and maintaining your clusters.

    Platform solutions

    Using IaaS only brings you so far for with big data applications: they handle the creation of computing and storage resources, but don’t address anything at a higher level. The set up of Hadoop and Hive or a similar solution is down to you.

    Beyond IaaS, several cloud services provide application layer support for big data work. Sometimes referred to as managed solutions, or platform as a service (PaaS), these services remove the need to configure or scale things such as databases or MapReduce, reducing your workload and maintenance burden. Additionally, PaaS providers can realize great efficiencies by hosting at the application level, and pass those savings on to the customer.

    The general PaaS market is burgeoning, with major players including VMware (Cloud Foundry) and Salesforce (Heroku, force.com). As big data and machine learning requirements percolate through the industry, these players are likely to add their own big-data-specific services. For the purposes of this article, though, I will be sticking to the vendors who already have implemented big data solutions.

    Today’s primary providers of such big data platform services are Amazon, Google and Microsoft. You can see their offerings summarized in the table toward the end of this article. Both Amazon Web Services and Microsoft’s Azure blur the lines between infrastructure as a service and platform: you can mix and match. By contrast, Google’s philosophy is to skip the notion of a server altogether, and focus only on the concept of the application. Among these, only Amazon can lay claim to extensive experience with their product.

    Amazon Web Services

    Amazon has significant experience in hosting big data processing. Use of Amazon EC2 for Hadoop was a popular and natural move for many early adopters of big data, thanks to Amazon’s expandable supply of compute power. Building on this, Amazon launched Elastic Map Reduce in 2009, providing a hosted, scalable Hadoop service.

    Applications on Amazon’s platform can pick from the best of both the IaaS and PaaS worlds. General purpose EC2 servers host applications that can then access the appropriate special purpose managed solutions provided by Amazon.

    As well as Elastic Map Reduce, Amazon offers several other services relevant to big data, such as the Simple Queue Service for coordinating distributed computing, and a hosted relational database service. At the specialist end of big data, Amazon’s High Performance Computing solutions are tuned for low-latency cluster computing, of the sort required by scientific and engineering applications.

    Elastic Map Reduce

    Elastic Map Reduce (EMR) can be programmed in the usual Hadoop ways, through Pig, Hive or other programming language, and uses Amazon’s S3 storage service to get data in and out.

    Access to Elastic Map Reduce is through Amazon’s SDKs and tools, or with GUI analytical and IDE products such as those offered by Karmasphere. In conjunction with these tools, EMR represents a strong option for experimental and analytical work. Amazon’s EMR pricing makes it a much more attractive option to use EMR, rather than configure EC2 instances yourself to run Hadoop.

    When integrating Hadoop with applications generating structured data, using S3 as the main data source can be unwieldy. This is because, similar to Hadoop’s HDFS, S3 works at the level of storing blobs of opaque data. Hadoop’s answer to this is HBase, a NoSQL database that integrates with the rest of the Hadoop stack. Unfortunately, Amazon does not currently offer HBase with Elastic Map Reduce.

    DynamoDB

    Instead of HBase, Amazon provides DynamoDB, its own managed, scalable NoSQL database. As this a managed solution, it represents a better choice than running your own database on top of EC2, in terms of both performance and economy.

    DynamoDB data can be exported to and imported from S3, providing interoperability with EMR.

    Google

    Google’s cloud platform stands out as distinct from its competitors. Rather than offering virtualization, it provides an application container with defined APIs and services. Developers do not need to concern themselves with the concept of machines: applications execute in the cloud, getting access to as much processing power as they need, within defined resource usage limits.

    To use Google’s platform, you must work within the constraints of its APIs. However, if that fits, you can reap the benefits of the security, tuning and performance improvements inherent to the way Google develops all its services.

    AppEngine, Google’s cloud application hosting service, offers a MapReduce facility for parallel computation over data, but this is more of a feature for use as part of complex applications rather than for analytical purposes. Instead, BigQuery and the Prediction API form the core of Google’s big data offering, respectively offering analysis and machine learning facilities. Both these services are available exclusively via REST APIs, consistent with Google’s vision for web-based computing.

    BigQuery

    BigQuery is an analytical database, suitable for interactive analysis over datasets of the order of 1TB. It works best on a small number of tables with a large number of rows. BigQuery offers a familiar SQL interface to its data. In that, it is comparable to Apache Hive, but the typical performance is faster, making BigQuery a good choice for exploratory data analysis.

    Getting data into BigQuery is a matter of directly uploading it, or importing it from Google’s Cloud Storage system. This is the aspect of BigQuery with the biggest room for improvement. Whereas Amazon’s S3 lets you mail in disks for import, Google doesn’t currently have this facility. Streaming data into BigQuery isn’t viable either, so regular imports are required for constantly updating data. Finally, as BigQuery only accepts data formatted as comma-separated value (CSV) files, you will need to use external methods to clean up the data beforehand.

    Rather than provide end-user interfaces itself, Google wants an ecosystem to grow around BigQuery, with vendors incorporating it into their products, in the same way Elastic Map Reduce has acquired tool integration. Currently in beta test, to which anybody can apply, BigQuery is expected to be publicly available during 2012.

    Prediction API

    Many uses of machine learning are well defined, such as classification, sentiment analysis, or recommendation generation. To meet these needs, Google offers its Prediction API product.

    Applications using the Prediction API work by creating and training a model hosted within Google’s system. Once trained, this model can be used to make predictions, such as spam detection. Google is working on allowing these models to be shared, optionally with a fee. This will let you take advantage of previously trained models, which in many cases will save you time and expertise with training.

    Though promising, Google’s offerings are in their early days. Further integration between its services is required, as well as time for ecosystem development to make their tools more approachable.

    Microsoft

    I have written in some detail about Microsoft’s big data strategy in Microsoft’s plan for Hadoop and big data. By offering its data platforms on Windows Azure in addition to Windows Server, Microsoft’s aim is to make either on-premise or cloud-based deployments equally viable with its technology. Azure parallels Amazon’s web service offerings in many ways, offering a mix of IaaS services with managed applications such as SQL Server.

    Hadoop is the central pillar of Microsoft’s big data approach, surrounded by the ecosystem of its own database and business intelligence tools. For organizations already invested in the Microsoft platform, Azure will represent the smoothest route for integrating big data into the operation. Azure itself is pragmatic about language choice, supporting technologies such as Java, PHP and Node.js in addition to Microsoft’s own.

    As with Google’s BigQuery, Microsoft’s Hadoop solution is currently in closed beta test, and is expected to be generally available sometime in the middle of 2012.

    Big data cloud platforms compared

    The following table summarizes the data storage and analysis capabilities of Amazon, Google and Microsoft’s cloud platforms. Intentionally excluded are IaaS solutions without dedicated big data offerings.

    Conclusion

    Cloud-based big data services offer considerable advantages in removing the overhead of configuring and tuning your own clusters, and in ensuring you pay only for what you use. The biggest issue is always going to be data locality, as it is slow and expensive to ship data. The most effective big data cloud solutions will be the ones where the data is also collected in the cloud. This is an incentive to investigate EC2, Azure or AppEngine as a primary application platform, and an indicator that PaaS competitors such as Cloud Foundry and Heroku will have to address big data as a priority.

    It is early days yet for big data in the cloud, with only Amazon offering battle-tested solutions at this point. Cloud services themselves are at an early stage, and we will see both increasing standardization and innovation over the next two years.

    However, the twin advantages of not having to worry about infrastructure and economies of scale mean it is well worth investigating cloud services for your big data needs, especially for an experimental or green-field project. Looking to the future, there’s no doubt that big data analytical capability will form an essential component of utility computing solutions.

    Notes:

    1 In public beta.

    2 In controlled beta test.

    3 Hive and Pig compatible.

    4 Experimental status.

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    Wipro Brings Big Data To Microsoft Azure Cloud Platform | killexams.com real questions and Pass4sure dumps

    cloud

    Wipro has announced the availability of Data Discovery Platform, its big data analytics-as-a-service solution on Microsoft Azure. The solution accelerates insight-driven decision making through pre-built applications for specific industries, such as banking and financial services, retail, energy, education and manufacturing.

    “The Data Discovery Platform will enable businesses to embark on an analytics journey with value added services of process simplification and business transformation to bridge the gap between the insights required by business and the information that is available,” the company said.

    Wipro Data Discovery Platform is available in an outcome-based ‘pay-per-insight’ cloud delivery model. The platform leverages Microsoft’s Cortana Intelligence Suite which includes HDInsight, Stream Analytics, Data Lake Analytics, Machine Learning and Power BI to build analytical applications. Currently, fourteen Wipro Data Discovery Platform applications have been showcased on Microsoft Advanced Analytics Partner Solution Showcase.

    Wipro and Microsoft have collaborated on various aspects of the solution, including engineering, solution enhancements, and joint go-to market strategy. “Together, Microsoft and Wipro have built an industry sector-specific apps ecosystem on the Data Discovery Platform.  Today, the platform is a significant enabler of Analytics led Digital Transformation delivering Analytics-as-a-Service to organizations. We believe that this is a reflection of the Wipro Data Discovery Platform’s maturity and Microsoft’s confidence in the prowess of this platform,” said Pallab Deb, Vice President & Global Head - Analytics, Wipro Limited.

    “Wipro’s Data Discovery Platform is a leading example of leveraging the data and Artificial Intelligence (AI) capabilities of Microsoft Azure cloud to build novel domain-specific business applications across industry verticals.  By developing pre-built domain specific apps on the platform, Wipro is able to accelerate time-to-value while ensuring scalability and performance. Wipro is well-positioned to build effective systems of intelligence for clients with the power of Cortana Intelligence, Power BI and Azure,” said Joseph Sirosh, Corporate VP, Cloud AI Platform, Microsoft.

    Wipro and Microsoft have been strategic partners for decades. Wipro has demonstrated its commitment to this relationship by investing in new industry-leading solutions for customers on the Azure platform.



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    References :


    Dropmark-Text : http://killexams.dropmark.com/367904/12988375
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    Calameo : http://en.calameo.com/books/0049235262d1ac5916f05
    zoho.com : https://docs.zoho.com/file/2xzfz212d9754d18d43a39ecb79d495774839
    publitas.com : https://view.publitas.com/trutrainers-inc/pass4sure-70-776-dumps-and-practice-tests-with-real-questions
    Box.net : https://app.box.com/s/l39ro6s7w60rjj6whyio8c5qn5qy6x15
    speakerdeck.com : https://speakerdeck.com/killexams/pass4sure-70-776-real-question-bank






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