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

Test Code : 70-776
Test designation : Performing gigantic Data Engineering with Microsoft Cloud Services
Vendor designation : Microsoft
: 69 true Questions

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Microsoft Performing gigantic Data Engineering

Panoply Is a Finalist In Microsoft AI and massive statistics competitors | killexams.com true 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 joyful to publish its fame as a finalist in Microsoft and Calcalist's ersatz Intelligence and gigantic records Startup competitors.

Panoply brand (PRNewsfoto/Panoply)

greater

In a inelastic competitors, Microsoft will host representatives from Panoply and the other finalists at its Seattle headquarters. the first and 2d area winners may breathe 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 supercilious to breathe announced as a finalist in the ersatz Intelligence and gigantic data Startup competitors. so far, we've been amazed on the first-rate of the corporations collaborating and we're psyched to breathe pitching at the finals. We're a team of engineers and fighters, they set lofty dreams and conquer gigantic challenges to prove how risky their ingenious wish is."

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

About PanoplyPanoply is a sage data stack that makes expend of computer discovering and herbal language processing to automate plenary 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

 

View fashioned content to download multimedia:http://www.prnewswire.com/information-releases/panoply-is-a-finalist-in-microsoft-ai-and-big-data-competitors-300763140.html


precise 10 Microsoft information and Analyses of 2018 | killexams.com true Questions and Pass4sure dumps

information

excellent 10 Microsoft tips and Analyses of 2018

here are the 12 months's most ordinary explainers and the way-to columns -- along with some simple, historical "Why did Microsoft Do that?" musings thrown in.

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

9. How Will windows Server 2019 believe an consequence on Hyper-V architecture?Microsoft's approaching home windows Server product appears to breathe choked with original points. listed here are one of the most standouts, specific to Hyper-V. examine perquisite here.

8. Spectre and Meltdown: How Do They believe an consequence on SQL Server?IT needs to patch everything, but the Spectre/Meltdown patches gain with their own complications, too. perquisite here's how to confine the fallout. study perquisite here.

7. the artery 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 legacy duty 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 entire laptop in windows 10. You simply believe to Do a bit digging to locate it. read perquisite here.

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

four. SQL Server 2019's gigantic records Clusters ExplainedThe biggest feature within the SQL Server 2019 preview launched at Ignite is SQL Server gigantic 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 original within the Public PreviewMicrosoft introduced the newest preview of its subsequent-gen SQL Server product at Ignite. perquisite here's a rundown of every limited thing the brand original release guarantees, from performance enhancements to safety and availability perks. examine here.

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

1. office 2019: Meet the brand original 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 perquisite here.


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

The open supply HPCC techniques platform is a confirmed, convenient to get expend of retort for managing information at scale. seek advice from their convenient e-book to breathe trained more about this fully free platform, check pressure some code within the on-line Playground, and collect plenary started today.

we believe gotten everybody connected to SQL Server using growth DataDirect's exclusive sheperd for each NTLM and Kerberos authentication from Linux with Sqoop. Now, they design to blow your minds with excessive-flying bulk insert performance into SQL Server the usage of Sqoop's frequent JDBC Connector. Linux clients will collect 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 dwelling from 10GB to 1TB into SQL Server information marts and warehouses. it breathe customary for the DataDirect SQL Server JDBC driver to pace up load instances via 15-20X, and Sqoop will note identical development due to the fact that it leverages JDBC batches that they transparently transmute into SQL Server's autochthonous 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 preempt if you are ready to examine on!

Get plenary started With swiftly efficiency for Sqoop EXPORT to SQL Server
  • download the DataDirect connect for JDBC drivers and solemnize 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 breathe /consumer/lib/sqoop/lib if you installed from an RPM or Debian equipment). The JDBC driver must breathe 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 hasten perquisite here question: choose identify, recovery_model_desc FROM sys.databases the dwelling identify = ‘database_name’ ; word the recovery_model_desc again by means of this question (expect to come, 'BULK_LOGGED').
  • From the command line, hasten 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 breathe distinct when the expend of a widely wide-spread JDBC connector.
  • --join is the JDBC URL. EnableBulkLoad=actual authorizes the DataDirect SQL Server driver to get the most of the bulk load protocol for the inserting of rows. The BulkLoadBatchSize value indicates to the motive coerce the number of rows it'll try to bulk load on a sole roundtrip to the server. If this value is below the sqoop.export.statistics.per.remark price, then every designation to executeBatch will result in more than one round vanish back and forth to the server in an endeavor to insert the batch of rows.
  • --table: The table to breathe populated in the goal relational database as records is transferred from HDFS.
  • --export-dir identifies the HDFS directory that includes the Hadoop table to breathe exported.
  • --input-strains-terminated-with the aid of identifies the character which separates rows within the HDFS files.
  • --enter-fields-terminated-by artery 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 serve 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 serve 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 pair of shops and the most efficient to date is a load of forty GB into SQL Server within ten minutes. within the above gadget, they 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 breathe challenging. learn the artery 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 collect begun these days.

    subject matters:

    massive statistics ,hadoop ,sql server ,sqoop ,information efficiency ,tutorial




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

    Test Code : 70-776
    Test designation : Performing gigantic Data Engineering with Microsoft Cloud Services
    Vendor designation : Microsoft
    : 69 true Questions

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    Performing gigantic Data Engineering with Microsoft Cloud Services

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

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

    Credit: Moon Light PhotoStudio

    The National Science Foundation (NSF) has announced a original cooperative agreement with Internet2, a nonprofit computer networking consortium, to build partnerships with commercial cloud computing providers and back science applications in original 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 gain of academic disciplines. Amazon Web Services and Google Cloud Platform believe 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 gain of applications that are necessary 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 breathe managed by Internet2 and is expected to relent a deeper understanding of the expend of cloud computing in accelerating scientific discoveries.

    "Internet2 looks forward to supporting NSF's vision as section of their 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 gain of science and facilitate more effective integration of cloud computing resources and cyberinfrastructure for leading-edge research. Internet2 is delighted that they can participate in this activity on behalf of their 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 gigantic Data Sciences and Engineering (BIGDATA) and gigantic Data Regional Innovation Hubs and Spokes (BD Hubs and Spokes) programs over a three-year term that began in 2017.

    E-CAS Next Steps

    The E-CAS project will invite proposals from researchers across the plenary gain of science and engineering domains who believe 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 breathe 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 breathe the novel expend of heterogeneous hardware resources and platforms, such as Central Processing Units, Graphics Processing Units and Field-Programmable Gate Arrays to back and extend scientific applications and workflows.

    E-CAS will believe two phases. The first aspect will back 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 aspect 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 embrace 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 faith and identity framework.

    ###

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


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

    Big data and cloud technology vanish hand-in-hand. gigantic data needs clusters of servers for processing, which clouds can readily provide. So goes the marketing message, but what does that Look infatuation in reality? Both “cloud” and “big data” believe 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 gigantic Data Market Survey can breathe deployed on IaaS services.

    Using IaaS clouds doesn’t spell you must handle plenary deployment manually: remarkable intelligence for the clusters of machines gigantic data requires. You can expend 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 plane to present a uniform interface to both private data centers and the public cloud. Attracting a strong flux of cross industry support, OpenStack currently addresses computing resource (akin to Amazon’s EC2) and storage (parallels Amazon S3).

    The race is on to get 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 breathe a uniform interface, whether you’re using public or private cloud facilities, or a hybrid of the two.

    Particular to gigantic data, several configuration tools already target Hadoop explicitly: among them Dell’s Crowbar, which aims to get 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 option of cloud supplier, the option of using a private cloud, and complete control: but you’ll breathe responsible for deploying, managing and maintaining your clusters.

    Platform solutions

    Using IaaS only brings you so far for with gigantic 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 back for gigantic data work. Sometimes referred to as managed solutions, or platform as a service (PaaS), these services remove the necessity to configure or scale things such as databases or MapReduce, reducing your workload and maintenance burden. Additionally, PaaS providers can realize remarkable efficiencies by hosting at the application level, and pass those savings on to the customer.

    The common PaaS market is burgeoning, with major players including VMware (Cloud Foundry) and Salesforce (Heroku, force.com). As gigantic 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 breathe sticking to the vendors who already believe implemented gigantic data solutions.

    Today’s primary providers of such gigantic data platform services are Amazon, Google and Microsoft. You can note their offerings summarized in the table toward the cease of this article. Both Amazon Web Services and Microsoft’s Azure blur the lines between infrastructure as a service and platform: you can amalgamate 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 pretension to extensive suffer with their product.

    Amazon Web Services

    Amazon has significant suffer in hosting gigantic data processing. expend of Amazon EC2 for Hadoop was a favorite and natural fling for many early adopters of gigantic 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. common purpose EC2 servers host applications that can then access the preempt special purpose managed solutions provided by Amazon.

    As well as Elastic Map Reduce, Amazon offers several other services germane to gigantic data, such as the Simple Queue Service for coordinating distributed computing, and a hosted relational database service. At the specialist cease of gigantic data, Amazon’s towering 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 breathe programmed in the customary Hadoop ways, through Pig, Hive or other programming language, and uses Amazon’s S3 storage service to collect 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 expend EMR, rather than configure EC2 instances yourself to hasten Hadoop.

    When integrating Hadoop with applications generating structured data, using S3 as the main data source can breathe unwieldy. This is because, similar to Hadoop’s HDFS, S3 works at the plane of storing blobs of opaque data. Hadoop’s retort to this is HBase, a NoSQL database that integrates with the ease of the Hadoop stack. Unfortunately, Amazon does not currently present 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 option than running your own database on top of EC2, in terms of both performance and economy.

    DynamoDB data can breathe 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 necessity 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 expend Google’s platform, you must travail within the constraints of its APIs. However, if that fits, you can gleam the benefits of the security, tuning and performance improvements inherent to the artery Google develops plenary 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 expend as section of complex applications rather than for analytical purposes. Instead, BigQuery and the Prediction API contour the core of Google’s gigantic data offering, respectively offering analysis and machine learning facilities. Both these services are available exclusively via ease 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 tiny number of tables with a great number of rows. BigQuery offers a close SQL interface to its data. In that, it is comparable to Apache Hive, but the typical performance is faster, making BigQuery a remarkable option 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 latitude for improvement. Whereas Amazon’s S3 lets you mail in disks for import, Google doesn’t currently believe 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 necessity to expend external methods to immaculate 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 very artery Elastic Map Reduce has acquired utensil integration. Currently in beta test, to which anybody can apply, BigQuery is expected to breathe 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 travail by creating and training a model hosted within Google’s system. Once trained, this model can breathe used to get predictions, such as spam detection. Google is working on allowing these models to breathe shared, optionally with a fee. This will let you acquire advantage of previously trained models, which in many cases will redeem 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 get their tools more approachable.

    Microsoft

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

    Hadoop is the central pillar of Microsoft’s gigantic data approach, surrounded by the ecosystem of its own database and business intelligence tools. For organizations already invested in the Microsoft platform, Azure will picture the smoothest route for integrating gigantic 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 breathe 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 gigantic data offerings.

    Conclusion

    Cloud-based gigantic data services present 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 breathe data locality, as it is late and expensive to ship data. The most effective gigantic data cloud solutions will breathe 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 believe to address gigantic data as a priority.

    It is early days yet for gigantic data in the cloud, with only Amazon offering battle-tested solutions at this point. Cloud services themselves are at an early stage, and they will note 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 spell it is well worth investigating cloud services for your gigantic data needs, especially for an experimental or green-field project. Looking to the future, there’s no doubt that gigantic data analytical capability will contour 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.

    Strata 2012 — The 2012 Strata Conference, being held Feb. 28-March 1 in Santa Clara, Calif., will present three plenary days of hands-on data training and information-rich sessions. Strata brings together the people, tools, and technologies you necessity to get data work.

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

    cloud

    Wipro has announced the availability of Data Discovery Platform, its gigantic data analytics-as-a-service solution on Microsoft Azure. The solution accelerates insight-driven determination making through pre-built applications for specific industries, such as banking and monetary 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 believe been showcased on Microsoft Advanced Analytics colleague Solution Showcase.

    Wipro and Microsoft believe collaborated on various aspects of the solution, including engineering, solution enhancements, and joint go-to market strategy. “Together, Microsoft and Wipro believe 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. They 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 ersatz 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 believe been strategic partners for decades. Wipro has demonstrated its commitment to this relationship by investing in original industry-leading solutions for customers on the Azure platform.



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