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70-773 | Analyzing Big Data with Microsoft R

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70-773 - Analyzing Big Data with Microsoft R - braindump

Vendor Microsoft
Exam Number 70-773
Exam Name Analyzing Big Data with Microsoft R
Questions 39 Q & A
Recent Update December 12, 2018
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70-773 exam Dumps Source : Analyzing Big Data with Microsoft R

Test Code : 70-773
Test Name : Analyzing Big Data with Microsoft R
Vendor Name : Microsoft
Q&A : 39 Real Questions

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Microsoft Analyzing Big Data with

Microsoft teams with answer ALS, makes $1M donation to bold huge data undertaking | killexams.com Real Questions and Pass4sure dumps

Former seasoned soccer player Steve Gleason helped to inspire the answer ALS venture, and Microsoft’s focus on the neurological ailment. photograph by means of Lucien Knuteson.

a couple of years ago, former football player Steve Gleason got here to Microsoft with a challenge: enhance technology to ease the effects of amyotrophic lateral sclerosis (ALS), a progressive neurological disease that changed into robbing him of his skill to move his wheelchair and play with his son.

The company replied with know-how that allowed him to movement his wheelchair and communicate using a keyboard by moving his eyes.

Now, answer ALS — a mission that also originated from a Gleason effort — is looking past expertise as an aid, harnessing the vigour of large information in the hopes of enhanced understanding the disorder and eventually constructing cures. They’re in the middle of a 5-yr challenge to accumulate 6 billion records facets each and every from 1,000 people with ALS. Thursday morning, Microsoft announced it'll make contributions to the groundbreaking effort with a $1 million donation in cloud computing and technical services to help researchers access that monstrous statistics.

“it's an amazingly bold project,” noted Elizabeth Bruce, Microsoft’s university members of the family director. “There’s been a lot of promise about how huge information can alternate nearly each field – here is the use of large statistics for good and saving lives.”

A path to medicine

within the u.s., about 5600 individuals are diagnosed yearly with ALS, a disease that kills the neurons that handle the body’s voluntary muscular tissues. There is no generic cause or family unit historical past in more than ninety p.c of instances.

The venture’s hope is that the finished data set will allow researchers to eventually differentiate diverse subtypes of ALS, paving the way for customized treatments, and ultimately treatments.

The six billion statistics aspects per adult signify every thing from their DNA to their day by day habits. That vast trove of open-source statistics may be hosted on the cloud with the idea that global experts in computing device getting to know, artificial intelligence and information informatics can mine it for patterns and clues.

“It’s a virtual biopsy of the mind,” talked about Dr. Jeffrey Rothstein, a professor of neurology at Johns Hopkins university and the founder and director of the reply ALS software, called the task “the largest endeavor” the neurological field has viewed.

The dataset is fascinating to researchers international not only for its quantity however as a result of they can use the guidance without needing to recruit sufferers for medical trials, rushing up the procedure by means of up to 10 instances, he estimated.

Microsoft Joins the hassle

The statistics will be saved in Microsoft Azure, which ensures patient privacy. Microsoft will also be working with scientists to work out the premier method to manipulate and host the data within the cloud. The greater available the records to researchers, the extra doubtless they're to make use of it, Microsoft’s Bruce mentioned.

Supporters of the ALS effort say it’s on the forefront of a transforming into intersection between big facts and fitness. The national Institutes of health, for example, has carried out a pilot application to build and keep research statistics on a cloud within the hopes of accelerating biomedical discoveries.

“It’s a little bit of a frontier and a query: Is it going to cause a remedy?” Bruce stated. “It’s the scale of our capability to research this in new ways that’s wonderful.”

The timing of the Microsoft’s announcement comes just before Friday’s answer ALS online game Changer Gala, which should be held on the Sheraton Seattle inn.  Seattle appeared a chief choice for the fundraising gala because of the Gleason connection in addition to local company partners akin to Microsoft and Boeing, and supporters like Pearl Jam’s Mike McCready, noted Clare Durrett of answer ALS.

thus far, answer ALS has raised $32 million with $8 million still essential to reach their assignment purpose, she said.  Of the 1,000 patients necessary for the venture, the group has already recognized 929.


Opinion: massive information indicates large promise for feeding the area | killexams.com Real Questions and Pass4sure dumps

Digital innovations are set to radically change a global food device in disaster.

These improvements, although, are reliant on big sets of records to assist farmers make informed selections and predict crop consequences. From soil moisture to weather patterns and ancient yield, countries like the United States and the UK have a lot of “large information” to place to work. the united states branch of Agriculture for example has over a century of farm and public data to draw from.

but why does this count number?

Ask any farmer, from California to Colombia, and they're going to inform you that unpredictable weather patterns are making farming more durable. The contemporary Intergovernmental Panel on climate trade report warned that global warming of two levels will cause corn yields to cut back through 15 per cent. In Africa, this could attain 20 per cent if temperatures upward thrust by using 3 levels, causing corn construction to wholly fall down in some areas.

Crunching large datasets youngsters, is more and more enabling farmers to visualize this uncertain future, and prepare for it.

The college of Minnesota, as an instance, has used advanced computer modelling to predict a number of future scenarios for corn creation, the usage of Iowa as a microcosm for international production. The look at showed that globally, corn yields may drop between 15 and 50 per cent, because of hotter air and cycles of drought followed by heavy rain. here is likely to happen when the area’s inhabitants hits 9 billion, resulting in a rise in as much as 60 per cent for meals needs.

This abilities is vigour. Farmers can now act to offer protection to yields, investing in expertise reminiscent of heat-tolerant crop breeds.

This category of labor has large to talents to advantage the rest of the area, the place greater people are going hungry. Most nations within the setting up world, youngsters, have a “black hole” of farm facts. This makes predictions and preparations like those carried out at the university of Minnesota inconceivable.

Public analysis networks like CGIAR, are working to bridge this records hole in collaboration with universities and corporations.

the primary stumbling block is that sophisticated technologies like smart sensors and cameras that farmers can use to assemble and analyze facts are priced out of reach for small-scale farmers in poorer countries. They may additionally live in far flung areas, with out an internet connection.

it really is why agencies like Microsoft are working on options to scale down the charge of digital farming technologies, and coming up with alternative routes to get small-farmers linked.

The FarmBeats mission as an instance, has developed a low-cost alternative to a drone that can catch farm information from the sky. “Tethered Eye” helium balloons are used to act as aerial sensors, generating pictures of farm conditions. These are then used to refine the records amassed via sensors on the ground. It’s precise facts collection with out the thousand-dollar cost tag.

To transmit this records, FarmBeats is experimenting with using television white area as an alternative of WiFi, an choice which is each cheap and plentiful in developing regions.

as soon as this data has been gathered, it is essential to make sure it is attainable to small farmers in an open and democratic manner. programs just like the CGIAR Platform for big statistics in Agriculture , which leverages the work and financing of assorted partners including the realm financial institution, have been install to make datasets created by means of these innovative methods freely accessible to all.

In Kenya, as an instance, where no effective records on the state of water elements within the Sondu-Miriu River basin exists, researchers have proven an method to crowdsource this records from citizens. Three water-level gauges had been erected with indications that defined the undertaking and requested passers-through to send measurements from the gauges via SMS. The records they got was cross-checked with a great deal extra costly automatic gauging stations regional and became found to be incredibly accurate.

in other places in Africa, free AI-powered apps are assisting farmers become aware of pest and ailment outbreaks in cassava. The app called Nuru changed into designed by using Penn State college in collaboration with CGIAR and Google the usage of TensorFlow, an open supply application for object cognizance. It changed into crafted via taking 11,670 pictures, to create 2,756 photos of leaves. After consultants diagnosed the illnesses, the photographs were equipped into a database which was used to instruct the application the usage of laptop discovering to admire the indicators of cassava pests and illnesses. The app is consumer-pleasant, and farmers or extension brokers with ease aspect their smartphone camera at a couple of cassava leaves and Nuru responds with a prognosis. it may well also be used offline, which is vital in remote farm communities.

Datasets which will inform the future of farming and offer protection to meals elements into the long run are growing to be. in the event that they will also be made available to all kinds of farmers, from huge to small, wealthy and terrible, we can catalyse an guidance revolution a good way to radically change our international food gadget for the enhanced.

Juergen Voegele is Senior Director of the food and Agriculture global practice on the World financial institution and Chair of the CGIAR device Council.


massive information LDN: one hundred companies, Attendees gear Up | killexams.com Real Questions and Pass4sure dumps

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Fourth Industrial Revolution report may be unveiled for the primary time completely to attendees

Over 100 companies, from Google to IBM, along with ratings of startups are gearing up for the annual huge statistics LDN conference, which kicks off Tuesday November 13.

The conference comes as a record by Unravel information found that 74 percent of corporations predict their big statistics stack to drive reliable, effective, and profitable company purposes by way of the conclusion of 2019, however handiest 12 per cent are seeing this value at existing.

“Most firms have high hopes for what big records functions can do for them – and rightly so,” stated Kunal Agarwal, CEO, Unravel information.

big data ldn

“but official performance remains some way off, which could extend the advantages agencies hunger after. The problem now could be to be sure the large statistics stack performs reliably and effectively, so the subsequent era of applications, throughout analytics, AI and computer getting to know, can deliver on those aspirations.

“only then may we see firms flip their mind-set from pondering of big records as gasoline for defensive actions, like cybersecurity and compliance, to seeing it as a catalyst for business increase.”

audio system… 

audio system from corporations including the NHS, BT, Vodafone, HMRC, Sky, GE Digital, Oracle, Coca-Cola, O2, Nvidia ING financial institution, Thames Water, Camden Council and WPP are all joining the annual adventure at Olympia London.

movements… 

Alan Mak MP, one of the vital architects of the united kingdom’s publish-Brexit statistics economy, and Chair of the Parliamentary Committee for the Fourth Industrial Revolution will give the keynote speech on the united kingdom government’s future massive data strategy in the facts-pushed Theatre at 10:30 am, on Tuesday 12th November.

Lewis Hamilton’s Mercedes AMG Petronas F1 team may be providing a car in a data-enabled pit lane.

The top notch facts Debate, that includes MIT Professor Dr. Michael Stonebraker, senior executives from IBM, Microsoft and Cloudera as well as BT Chief data Architect Phillip Radley, will look into the way forward for statistics administration and desktop intelligence.

This year’s Fourth Industrial Revolution file could be unveiled for the primary time solely to attendees, full of insights, forecasts and evaluation on the uk’s area in nowadays’s synthetic Intelligence-led financial system.

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70-773 exam Dumps Source : Analyzing Big Data with Microsoft R

Test Code : 70-773
Test Name : Analyzing Big Data with Microsoft R
Vendor Name : Microsoft
Q&A : 39 Real Questions

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Analyzing Big Data with Microsoft R

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10 Best Big Data Analytics Courses Online | killexams.com real questions and Pass4sure dumps

Data analytics skill has become one of the most important factors in one’s career development today as data has become more explosive than ever. According to PWC (Price Waterhouse Coopers), over 50 million jobs were created in “analytics skills” from 2015 to 2018.

This article will recommend the 10 best online courses on different MOOC for beginners to learn data science fundamentals, key data science tools, and widely-used programming languages in big data analytics.

“Acquiring an education is the best way to not be left behind as technology races ahead.”

- The Second Machine Age

Coursera

1. Data Analysis and Presentation Skills: the PwC Approach Specialization

Creator: Price Waterhouse Coopers LLP

Commitment: 21weeks, 3–4hours/week

This specialization includes 5 courses, from data-driven decision making, problem-solving with basic functions of Excel, data visualization with advanced excel, to the business presentation with PowerPoint, and a final project.

With an average rating of 4.6, this data analysis specialization is designed for employees by PWC, which undoubtedly focuses more on business application than theory. And it’s suitable for people without a programming background.

2. Data Science Specialization

Creator: John Hopkins University

Commitment: 43 weeks, 4–9 hours/week

Composed of 10 courses, this specialization covers the concepts and tools you’ll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results.

Rating 4.6, this is one of the longest data science specializations on Coursera. Unlike the PWC one, it focuses more on theories relating to statistic, algorithm and data analysis. Besides, it practices mainly by R programming, so a basic knowledge of programming is recommended before taking this specialization.

3. Big Data Specialization

Creator: University of California, San Diego

Commitment: 30 weeks, 3–6 hours/week

With a total 6 courses, it covers the main aspects of big data, from the basic introduction, modeling, management systems, integration, and processing, to machine learning and graph analytics.

Average rating is 4.3. This is a great introduction to big data for beginner and doesn’t delve too much into programming., no prior programming experience is needed. As it involves several open-source software tools, including Apache Hadoop, it has requirements on hardware and software.

4. Statistics with R

Creator: Duke University

Commitment: 27 weeks, 5–7 hours/week

With the 5 courses in this specialization, you will learn to analyze and visualize data in R and create reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform frequentist and Bayesian statistical inference and modeling.

Average Rating of 4.5, the course is all about R programming, which is a free software environment for statistical computing and graphics.

EDX

5. Microsoft Professional Program in Data Science

Creator: Microsoft

Commitment: 56–58 weeks, 2–4 hours/week

Made up of 3 units (9 courses total) and a Final Project, this specialization covers the basic introduction of data science, essential programming languages and advanced programming languages in applied data science.

Unsurprisingly, it has a high connection with Microsoft software, including Excel, Power BI, Azure and R server. The programming languages involved are R and Python. If above-mentioned software utilities are your daily utilities, then you should not miss it.

6. Marketing Analytics

Creator: University of California, Berkeley

Commitment: 16 weeks, 5–7 hours per week.

The program is designed and taught by industry expert Stephan Sorger, who has held leadership roles in marketing and product development at companies such as Oracle, 3Com, and NASA.

This program is divided into 4 courses, including Marketing Measurement Strategy, Price and Promotion Analytics, Competitive Analysis and Market Segmentation, and the last one, Products, Distribution and Sales.

Cognitive Class

7.Big Data Fundamentals

Creator: IBM

Commitment: 13 hours

It only consists of 3 courses, which give a brief introduction on Big Data, Hadoop and Spark. Known as Big Data University before, Cognitive Class is a MOOC provided by IBM.

MIT Open Courseware

8. Advanced Data Structures

Instructor: Prof. Erik Demaine

Commitment: 22 sessions, 90mins/session

This course serves as a broad overview of the many different types of data structures, including geometric data structures, like a map, and temporal data structures, as in storage that happens over a time series. It covers the major directions of research for a wide variety of such data structures.

Pluralsight

9. Python

Instructors: Austin Bingham, Robert Smallshire, Terry Toy, Bo Milanovich, Emily Bache, Reindert-Jan Ekker

Commitment: 7 courses, 28 hours totally

This path will take you from the basics of the Python language all the way up to working with web frameworks and programming.

Python is an interpreted object-oriented programming language. It is open source, so the interpreter and source are freely available and distributable in binary form, which contributes Python become a popular programming language in data analysis.

Udemy

10. Java Tutorial for Complete Beginners

Instructor: John Purcell

Commitment: 75 lectures, 16 hours totally

A beginner course to learn the Java programming language, no prior programming knowledge required. The key reason for this course: Hadoop is Java-based, which is one of the hottest open-source software utilities that paves the ground for analyzing big data.

Octoparse

The above-mentioned courses involve different aspects of data analytics, but there is a presupposition — you’ve obtained enough data. Currently, the amount of data in the digital world doubles every two years. Traditional approaches to extract data online manually can no longer be used, you need a much more efficient web-scraping tool to extract information on the Internet.

Recommended by many data analysts, Octoparse is an automatic web scraping tool. It is easy to use, fast to learn and does not require prior programming knowledge. Millions of data online will turn into structured datasheet (Excel, CSV, SQL, API) at your fingertips in seconds.

An abundance of step by step tutorials on scraping different kinds of websites is available on Octoparse’s website, such scraping leads from directories (Yellowpages), and scraping product information from an online marketplace (Amazon).

The biggest challenge for you is not how difficult the courses might be, but taking responsibility of your own advancement and demonstrating your perseverance.

Happy Learning!


Comparing the leading big data analytics software options | killexams.com real questions and Pass4sure dumps

There are many vendors selling products classified as big data analytics software. However, it's challenging to...

differentiate these products based on functionality alone, as many of the tools share similar features and capabilities. Additionally, some of the tools exhibit extremely subtle differences.

That being said, your key differentiating factors will likely focus on balancing ease of use, algorithmic sophistication and price in relation to your organization's capability and level of maturity in analytics.

In this article, we examine products from nine big data analytics software vendors: Alteryx Inc., IBM, KNIME AG, Microsoft, Oracle, RapidMiner Inc., SAP, SAS Institute Inc. and Teradata Corp. Some of these vendors provide more than one tool. See the "Leading vendors of big data analytics software" sidebar below for more details about their specific product offerings.

These vendors represent different facets of the big data analytics market. Let's compare and contrast the ways that these products meet the business needs of user organizations.

Analyst expertise and skills 

Some data analytics tools are targeted to novice users, some are targeted to expert data analysts and some are engineered to appeal to both types of users.

Products such as IBM SPSS Modeler, RapidMiner's tools, Oracle Advanced Analytics and the Automated Analytics version of SAP BusinessObjects Predictive Analytics are generally designed to enable users with a limited background in statistics or data analysis to analyze data, develop analytical models and design analytics workflows with little or no coding.

While each vendor wraps its core analytics components with an intuitive user interface to guide the analyst's progress in data preparation, analysis, and then model design and validation, the approach taken may differ, especially when comparing a stand-alone product, such as RapidMiner, with one that's a component of a larger suite, such as the Oracle product.

Tools such as IBM SPSS Statistics, KNIME Analytics Platform, the Expert Analytics module of SAP BusinessObjects Predictive Analytics, Microsoft R and the Teradata Aster Analytics platform provide the more sophisticated functionality that expert users expect. Oracle R Advanced Analytics for Hadoop (ORAAH), one of the components in the Oracle Big Data Software Connectors suite, provides an R interface for manipulating Hadoop Distributed File System data and writing mapper and reducer functions in R. This flexibility may be appealing to more advanced data scientists.

Alteryx and SAS Enterprise Miner offer functionality adapted to the user's level of expertise, and essentially fall into both categories. Alteryx has added improvements to data profiling to help data scientists better understand their data sources. Overall, SAS Enterprise Miner and IBM's SPSS tools stand out when it comes to supporting more advanced analytical techniques and model scoring, as well as a broader array of analysis functions, including neural networks, association analysis and visualization capabilities.

Analytical diversity

Depending on the use case and application, your organization's users will be required to support different types of analytics capabilities that will use specific types of modeling, such as regression, clustering, segmentation, behavior modeling and decision trees.

While this has resulted in broad support for the various forms of analytical modeling at a high level, some vendors have invested decades of work into tweaking different versions of their algorithms and adding more sophisticated functionality. It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

It's important to understand which models are most relevant to your business problems and to evaluate the products in terms of how they best serve your users' business needs.

The more mature and higher-end -- and, accordingly, higher-priced -- tools will exhibit the greatest analytical breadth. Oracle Data Miner includes an array of well-known machine learning approaches to support clustering, predictive mining and text mining. Both editions of IBM's SPSS product provide a diverse set of analytical techniques and models. And SAS Enterprise Miner supports many algorithms and techniques, including decision trees, time series, neural networks, linear and logistic regression, sequence and web path analysis, market basket analysis, and link analysis.

The newer generation -- and, in some cases, lower-priced -- products support different models, but perhaps with a narrower range of algorithmic sophistication.

The model inventory in Alteryx Analytics Gallery includes such capabilities as regression analysis, decision trees, association rule analysis, classification and time series analysis. KNIME includes methods for text mining, image mining and time series analysis, and also integrates machine learning algorithms from other open source projects, such as Weka and JFreeChart.

Another aspect of analytical diversity is integration with programming languages and statistical tools, such as R, for incorporating existing libraries, as well as user-defined functionality. In fact, integration with R could be considered an increasingly critical differentiator.

Alteryx Designer, Microsoft R, SAS Enterprise Miner, Teradata Aster Analytics, Oracle's ORAAH and KNIME's Analytics Platform all interface and support integration with R. Several of the vendors, including IBM, Oracle, Microsoft, RapidMiner and SAP, provide a growing library of extensions to R and Python, enabling users to take advantage of free libraries.

Scope of the data to be analyzed

There are multiple facets of the scope of the data to be analyzed, including the issue of structured vs. unstructured information, as well as access to conventional on-premises databases and data warehouses, cloud-based data sources, and data managed in big data platforms, such as Hadoop.

However, there are varying degrees of support for data managed within less-conventional data lakes -- either managed within Hadoop or in another NoSQL data management system intended to provide horizontal scaling. The factors for distinguishing among the products must be based on your organization's specific requirements for accessing and processing data volumes and data variety.

In recognition of the growing diversity of input sources and the variety of underlying systems used to house those data sets, another set of emerging features that is being adopted by these vendors involves data accessibility. IBM, RapidMiner, Alteryx, Oracle and Microsoft have all improved their tools' data import, export and connectivity capabilities. These enhancements should enable users to access a more comprehensive list of data sources while simplifying and speeding up the process of loading data into the products.

Support for scalability and high performance

The need for scalable performance is driven by your organization's data volumes and appetite for analysis. Smaller organizations with less data may be able to tolerate products that don't have performance characteristics that scale with the available resources, such as the entry-level versions of the lower-end tools, including RapidMiner, KNIME, Microsoft R Open and Alteryx Designer, which can run on desktop systems and don't require additional server components.

Larger organizations are more likely to have a greater inventory of data sets to analyze, as well as broader communities of users. This introduces two additional requirements -- high performance and facilitation of collaboration. The adaptability of a product to high-performance architectures is a good indication of scalability, and most of the products can be adapted to the parallelism of Hadoop or employ some other means of achieving faster computation.

All of the products do have some support for Hadoop, including IBM SPSS Modeler and SPSS Statistics; RapidMiner's commercial component Radoop, which connects the Studio front end and Server analysis engine to data stored in Hadoop; Oracle's Big Data Discovery and ORAAH tools; and KNIME's Big Data Extensions and Cluster Execution add-ins.

IBM SPSS now also provides enhanced support for a number of multithreaded analytical algorithms that may speed performance. Teradata Aster Analytics addresses high-performance requirements through its Massively Parallel Processing architecture. SAP's Expert Analytics version of SAP BusinessObjects Predictive Analytics can execute in-memory data mining for handling large-volume data analysis efficiently. Microsoft R Server leverages its ScaleR module, a comprehensive library of big data analytics algorithms that support parallelization. Scoring algorithms implemented using SAS Enterprise Miner can be deployed and executed within a Hadoop environment.

In addition, integration with Apache Spark appears to be of growing importance. SPSS, KNIME, Oracle, RapidMiner and SAP all provide access to Apache Spark libraries to support analytics applications that need to scale with exploding data volumes. This enables developed applications to take advantage of a high-performance cluster platform to distribute the workflow across the cluster.

Collaboration

As noted, the larger the organization, the more likely there will be a need to share analyses, models and applications across different groups and among many analysts. Organizations that have many analysts distributed across the enterprise may look for increased means to share models and collaborate regarding the interpretation of results.

IBM's SPSS Modeler Gold edition provides collaboration capabilities, and RapidMiner's Server product provides support for sharing and collaboration. Alteryx Analytics Gallery provides a mechanism for sharing sophisticated analytics applications in the cloud with members of an extended organization. KNIME offers commercial extensions to support team collaboration, as well as extensions supporting operational collaboration, such as remote-scheduled execution, report generation, shared data space and a workflow repository. SAS Enterprise Miner's client-server architecture enables business users and data analysts to work collaboratively by sharing models and other work products.

Alteryx, KNIME and Teradata Aster have added capabilities to help manage analytical workflows. Also, some of the vendors have started to look at ways to enable their tools to integrate with others that may have complementary functional sweet spots. For example, Teradata Aster now has an extension to integrate with KNIME that enables users to leverage the KNIME workflow editor and incorporate Aster Analytics functions into those workflows.

Vendor size and product integration

Vendors can be compared in terms of their size. One might compare and contrast what could be referred to as the mega-vendors, whose big data analytics tools are just one product among a massive portfolio of tools. If you work for a larger organization that typically negotiates site-wide, enterprise licenses for the full suite of a vendor's tools from a mega-vendor such as IBM, SAS, SAP or Oracle may be a reasonable choice.

The large vendors sell big data analytics tools that are a part of a much larger tool ecosystem. Presumably, the products from a mega-vendor will be at least somewhat integrated and intended to work together. In addition, some people feel more comfortable with bigger vendors, with an expectation of stability and consistent customer service. On the other hand, you may only be able to acquire these big data analytics tools as part of a much larger software licensing arrangement.

Smaller vendors, such as KNIME, Alteryx and RapidMiner, have revenues that are generally based on licensing and support for a small number of big data analytics products. A smaller vendor may provide closer contact with their product management and innovation teams, and you may be able to influence the direction of the product roadmap or enhanced functionality.

A smaller vendor might also be more flexible in terms of price and the features included in the licensing arrangement. You must realize, however, that working with a smaller vendor does present some risk in terms of stability, the resources available for support and the possibility that the company may be acquired, which can impact the customer relationship.

The larger vendors are clearly responsive to user needs for integration with other systems, although that often centers on other products within each vendor's inventory. For example, SAP Predictive Analytics has improved integration with SAP HANA and BusinessObjects Cloud. SAS Enterprise Miner has added nodes to execute code in a SAS open, cloud-ready, in-memory Viya environment. Microsoft offers SQL Server R Services, an R installation that runs alongside SQL Server and enables users to integrate Microsoft R Server data with SQL Server and Microsoft's other business intelligence tools.

Budget for licensing and maintenance

Almost all of the vendors sell different versions or editions of their products, with a range of costs for acquisition and total cost of operation. IBM, Oracle, RapidMiner, Teradata and Microsoft sell editions at different tiers, with the license cost proportional to the features, capabilities and freedom from limitations in terms of the volumes of data to be analyzed or the number of processing nodes the product can use.

KNIME and RapidMiner provide free and open source versions of their products, either charging for support services or for editions supporting enterprise-class applications. KNIME, RapidMiner and Alteryx have relatively low licensing costs for a smaller number of users. If you're considering SAS or SAP, you must contact them for pricing alternatives.

The marketplace for big data analytics software can be a confusing place, but hopefully this article has helped you understand the benefits big data analytics software can provide your organization, and assisted you in differentiating between the specific tools examined here.


Analyze Elasticsearch Data in R | killexams.com real questions and Pass4sure dumps

Java-based (JDBC) data connectivity to SaaS, NoSQL, and Big Data. Download Now.

You can access Elasticsearch data with pure R script and standard SQL on any machine where R and Java can be installed. You can use the CData JDBC Driver for Elasticsearch and the RJDBC package to work with remote Elasticsearch data in R. By using the CData Driver, you are leveraging a driver written for industry-proven standards to access your data in the popular, open-source R language. This article shows how to use the driver to execute SQL queries to Elasticsearch and visualize Elasticsearch data by calling standard R functions.

Install R

You can match the driver's performance gains from multithreading and managed code by running the multithreaded Microsoft R Open or by running open R linked with the BLAS/LAPACK libraries. This article uses Microsoft R Open 3.2.3, which is preconfigured to install packages from the Jan. 1, 2016 snapshot of the CRAN repository. This snapshot ensures reproducibility.

Load the RJDBC Package

To use the driver, download the RJDBC package. After installing the RJDBC package, the following line loads the package:

library(RJDBC) Connect to Elasticsearch as a JDBC Data Source

You will need the following information to connect to Elasticsearch as a JDBC data source:

  • Driver Class: Set this to cdata.jdbc.elasticsearch.ElasticsearchDriver.
  • Classpath: Set this to the location of the driver JAR. By default, this is the lib subfolder of the installation folder.
  • The DBI functions, such as dbConnect anddbSendQuery , provide a unified interface for writing data access code in R. Use the following line to initialize a DBI driver that can make JDBC requests to the CData JDBC Driver for Elasticsearch:

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

    You can now use DBI functions to connect to Elasticsearch and execute SQL queries. Initialize the JDBC connection with the dbConnect function. Below is a typical JDBC connection string:

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

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

    The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.

    Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.

    Schema Discovery

    The driver models Elasticsearch APIs as relational tables, views, and stored procedures. Use the following line to retrieve the list of tables:

    dbListTables(conn) Execute SQL Queries

    You can use the dbGetQuery function to execute any SQL query supported by the Elasticsearch API:

    orders <- dbGetQuery(conn,"SELECT Orders.Freight, Customers.ContactName FROM Customers INNER JOIN Orders ON Customers.CustomerId=Orders.CustomerId")

    You can view the results in a data viewer window with the following command:

    View(orders) Plot Elasticsearch Data

    You can now analyze Elasticsearch data with any of the data visualization packages available in the CRAN repository. You can create simple bar plots with the built-in bar plot function:

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

    Topics:

    big data ,elasticsearch ,r ,data analysis ,tutorial



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