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70-776 - Performing Big Data Engineering with Microsoft Cloud Services - BrainDump Information

Vendor Name : Microsoft
Exam Code : 70-776
Exam Name : Performing Big Data Engineering with Microsoft Cloud Services
Questions and Answers : 69 Q & A
Updated On : December 17, 2018
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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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  • provide chain margins
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  • volume drivers impacting pricing
  • part 04: can charge-Saving alternatives

  • supplier-side levers
  • purchaser-side levers
  • Quantifying charge-saving opportunities
  • Quantifying cost reductions through negotiation options
  • part 05: optimum Practices

  • Innovation and success reviews
  • Procurement excellence finest practices
  • Procurement finest practices
  • Sustainability practices
  • part 06: class Ecosystem

  • Market favorability index for suppliers
  • Competitiveness index for suppliers
  • buyer vigor
  • enterprise vigor
  • chance of latest entrants
  • danger of substitutes
  • buyer ecosystem
  • organisation ecosystem
  • part 07: category administration strategy

  • class administration ambitions
  • corporation and buyer KPIs
  • Outsourcing category management activities
  • chance management
  • half 08: category management Enablers

  • Procurement corporation
  • category enablers
  • part 09: Suppliers alternative

  • RFx necessities
  • agency selection standards
  • provider stage contract
  • Incumbent corporation comparison metrics
  • half 10: Suppliers below insurance

  • Overview
  • enterprise positioning
  • Profile: IBM
  • Profile: Amazon
  • Profile: Microsoft
  • Profile: Oracle
  • Profile: Alphabet
  • Profile: SAP
  • part 11: US Market Insights

  • category spend in the US
  • cost outlook
  • supplier cost structure in the US
  • Margins of suppliers in the US
  • class charge drivers
  • category volume drivers
  • Overview of most beneficial practices in the US
  • Procurement most fulfilling practices within the US
  • US provide market overview
  • business enterprise positioning for the us
  • Regional category hazards
  • part 12: class Definition

  • class hierarchy
  • category scope
  • class map
  • For greater counsel about this document discuss with https://www.researchandmarkets.com/analysis/bgpwmq/big_data_as_a?w=four


    Azure facts Studio (formerly SQL Operations Studio) | killexams.com Real Questions and Pass4sure dumps

    Azure information Studio is a free Microsoft computing device device, initially known as SQL Operations Studio, that can be used to control SQL Server databases and cloud-primarily based Azure SQL Database and Azure SQL facts Warehouse techniques. The lightweight application is designed to make pursuits database development, querying and administration work less demanding than it is with Microsoft's extra-functional SQL Server management Studio (SSMS) software. as a result, Azure facts Studio is aimed now not only at database administrators, but additionally at application builders and other non-DBAs who manage databases as part of their jobs.

    also known as SQL Ops Studio in its customary incarnation, the device offers a consistent user event throughout windows, Linux and macOS shoppers. It first became purchasable as a public-preview technology in November 2017 and remained in preview mode except September 2018, when Microsoft made the product commonly available and adjusted its name to Azure information Studio. The enterprise has issued new releases with introduced elements on a month-to-month foundation considering launching the SQL Operations Studio preview, a practice it plans to continue with the GA version.

    elements of Azure information Studio

    Azure information Studio uses Microsoft's visual Studio Code supply code editor as its foundation; the administration tool become forked from visible Studio Code, and as in the latter know-how, the open supply Electron framework serves as a cross-platform application building block. An integrated terminal window allows users to execute database administration instructions with Bash, PowerShell, sqlcmd and different command-line equipment from within Azure facts Studio's UI.

    The device contains developed-in features like multi-tab windows, a T-SQL editor, sensible code navigation and integration with the Git open source edition handle device for tracking source code. in the T-SQL editor, users can shop the results of database queries as textual content, JSON or Excel information; apart from question development, the editor supports the creation of kept methods, scripts and different database objects.

    Azure statistics Studio also provides a collection of T-SQL code snippets that may also be used to streamline the system of writing T-SQL statements. The code snippets are templates that generate the T-SQL syntax needed to operate different administrative actions, from updating existing database objects to creating new databases, tables and views. users can additionally create customized code snippets for moves that are not covered by means of the built-in ones.

    User interface in Microsoft's Azure Data Studio tool A screenshot of Azure facts Studio's user interface, with the management dashboard for a SQL Server system displayed.

    one more key characteristic in Azure facts Studio is the ability to create perception widgets that visualize the outcomes of T-SQL queries to make it less difficult for DBAs and different users to peer what's going on in their databases. An perception widget uses JSON code to automatically generate a chart or graph that shows suggestions about a person database or a SQL Server system, depending on the T-SQL question. clients can drill down into the underlying information and initiate management actions in line with what they find.

    Azure statistics Studio helps SQL Server from the 2014 release to the SQL Server 2019 preview that Microsoft also made obtainable in September 2018. both Azure SQL Database and Azure SQL information Warehouse are additionally supported, and as of November 2018 the tool included preview assist for Azure SQL Database Managed example, a version of the cloud database equipment it really is virtually functionally comparable to SQL Server.

    advantages of the use of Azure records Studio

    users of Azure statistics Studio can create customizable dashboards to monitor and troubleshoot efficiency bottlenecks and other issues in databases and on the server level. The device contains separate server and database dashboards, which will also be configured with insight widgets to deliver at-a-glance views of efficiency levels, device usage and different metrics. apart from viewing that assistance, users can lower back up and restore databases, edit the facts in database tables and perform other management projects from internal the dashboards.

    A server companies feature allows for clients to arrange the servers and databases they work with into distinct groups to aid streamline management techniques, with color coding to differentiate between the organizations. The community configurations are saved in the tool's user settings and might be modified by means of an modifying characteristic that contains the skill to pull and drop servers from one server community to an extra.

    Microsoft MVP supply Fritchey suggests a way to writea database question in Azure facts Studio.

    Azure statistics Studio gives APIs and authoring equipment to prolong the tool's performance past what's within the base software. users can create their own extensions or take abilities of ones made obtainable by way of third-birthday party builders. Microsoft is also setting up extensions, including ones that comprise performance from SSMS.

    for example, the company brought guide for its SQL Server Agent job scheduling and execution tool by means of an extension in the April 2018 replace of the SQL Operations Studio preview; an SSMS wizard used to import flat files become similarly introduced in the August 2018 replace. And an extension for querying and managing the preview edition of SQL Server 2019 became brought as a part of the first Azure records Studio GA free up; that extension helps the massive records clusters enabled in the database replace and offers the first notebook-vogue development environment for SQL Server.

    whereas Microsoft says Azure records Studio wasn't built to change SSMS, the newer tool does offer equivalent styles of functionality in a more light-weight kit. Its move-platform client help additionally gives clients more flexibility than the home windows-most effective SSMS application does. And or not it's designed to be handy to make use of despite ability degree. due to this fact, there's less of a discovering curve for users who don't seem to be full-time DBAs than there is with SSMS, in keeping with Microsoft.

    Microsoft recommends that worker's use Azure information Studio in its place of SSMS if they essentially edit and run queries in opposition t databases and want to rapidly chart or visualize question outcomes; moreover, connecting to and querying a SQL Server 2019 big records cluster is supported only in Azure data Studio at latest. in spite of this, SSMS should still be used through DBAs and different IT professionals who spend most of their time on database administration initiatives, including deep configuration and security administration work, and wish to make use of performance tuning advisor equipment and dashboards, according to Microsoft.

    a way to get began with Azure facts Studio

    To set up Azure facts Studio, users down load the edition of the program for his or her customer platform from Microsoft's web site or GitHub and then either birth the application without delay or extract it from a .zip file, counting on which down load choice they've chosen. A Connection dialog field automatically opens when the device is first run to instant users to connect it to SQL Server or Azure SQL information shops, with authentication and password protections inbuilt to make certain that only approved users can achieve this.

    The Azure data Studio UI is split into three sections: an pastime Bar with a group of clickable icons on the left of the reveal, a much wider rail with hyperlinks to a variety of folders and lines, and the main window, which is the place dashboards and the T-SQL editor monitor. distinct tabs can also be open in the main window, enabling clients to run different dashboards and the query editor on the same time.

    The exact bar within the application allows for clients to execute or cancel queries; connect to and disconnect from servers and databases; and display estimated question execution plans. Azure records Studio additionally includes a bottom bar that provides access to the built-in terminal window and different facets.

    Two sorts of configuration settings are supported: user settings that are utilized universally, and workspace settings that override the commonplace ones in individual folders. To adjust the default settings, they can also be opened from the File menu or through pressing Ctrl + Shift + P. as an instance, after doing so, clients can change the color scheme in Azure facts Studio or the SQL Operations Studio preview releasae in the event that they're nevertheless running that with the aid of picking Preferences after which color Theme from the drop-down menu.

    Microsoft recommends that new users enable a "preview elements" environment by way of default once they down load Azure facts studio so that they can entry functionality that's obtainable handiest in preview mode in the event that they so want. meanwhile, present users of the SQL Operations Studio preview free up who want to maintain customized settings when they improve to Azure information Studio can reproduction the information of their user settings folder, save the info locally on their computer after which paste the contents into the same folder in Azure information Studio.




    Killexams.com 70-776 Dumps and Real Questions

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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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    Performing Big Data Engineering with Microsoft Cloud Services

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    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

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    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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