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70-773 - Analyzing Big Data with Microsoft R - BrainDump Information

Vendor Name : Microsoft
Exam Code : 70-773
Exam Name : Analyzing Big Data with Microsoft R
Questions and Answers : 39 Q & A
Updated On : October 19, 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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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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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


    Modern Data Warehouse Design Pattern – Part II | killexams.com real questions and Pass4sure dumps

    Introduction

    In a previous article we discussed Modern Data Warehouse designs patterns and components. In this article we will discuss two more modern design patterns to handle your scenarios; 1) Advanced Analytics on big data 2) Real time analytics.

    Advanced Analytics on big data and Real-time analytics are prime business needs these days and require a modern design using the latest technology components. Microsoft Azure provides a set of technology components to meet all your needs. Advanced Analytics can be achieved using Azure Machine Learning tools. A modern design helps to build and deploy custom machine learning models. These models can transform data into actionable insight. In the situation of analyzing and processing data from the Internet of Things (IoT), Weblog and live streaming data, a real-time analytics design pattern can be used.

    Design of Advanced Analytics on Big Data

    The Modern design of Advanced Analytics on big data integrates structured, semi-structured and unstructured data from various data sources using Azure Data Factory and stores it in Azure storage, Azure Data Lake or Azure Blob Storage. This is a common data ingest process like other data warehouse design patterns. In the next part of the design, once data is stored in Azure storage, Azure Databricks can be leveraged to clean and transform the unstructured data and combine the data with other available structured data from an operational database or data warehouse. Databricks gives flexibility to build and deploy machine learning models on-premises or in the cloud. The Azure Databricks capabilities can be explored more to derive deeper insight from data using Python, R or Scala with inbuilt notebook experiences in Azure Databricks. Now, data can be used various ways from here:

  • Data can be moved to Azure SQL Data Warehouse to access using Azure native connectors
  • Power BI users can take advantage of Databricks to perform root cause determination and raw data analysis
  • Business users can execute ad hoc queries directly on data within Azure Databricks
  • Data can be moved from Databricks to Azure Cosmos DB to make it accessible to web and mobile applications for greater insight
  • Advanced Analytics on Big Data DesignFigure 1 Advanced Analytics on Big Data Design

    Design of Real-time Analytics

    The modern design of Real-time Analytics starts with two parts of the data ingest process; one is ingestion of live streaming data using the Apache Kafka cluster in Azure HDInsight and the second is get the all structured/semi-structured data to Azure Data Lake or Azure Blob Storage using Azure Data Factory. In the next part of the design, data can be prepared and trained using Azure Databricks. Azure Databricks can be leveraged to clean and transform the live streaming data and combine the data with structured data from an operational database or data warehouse. The inbuilt notebook experiences in Azure Databricks can be used to apply machine learning/deep learning techniques to derive deeper insights from this data. Afterwards, data can be accessed in various ways using other Azure components.

    Data can be moved at any scale between Azure SQL Data Warehouse and Azure Databricks using native connectors. A set of analytical dashboards can be built, and reports can be embedded on top of Azure SQL Data Warehouse data to give insight to business users within the organization. Also, Azure Analysis Services can serve this data to other users.

    Power BI users can use Azure Databricks and Azure HDInsight to perform raw data analysis and root cause determination. In this design, Azure Cosmos DB is also leveraged to take the insight from Azure Databricks to Azure Cosmos DB, to make data accessible through real time mobile and web applications.

    Real-time Analytics DesignFigure 2 Real-time Analytics Design

    Components of Advanced Analytics on Big Data and Real-time Analytics

    The complete design consists of various technology components. We have leveraged many Azure components in the two design patterns above. Some of the Azure components are commonly used and discussed in a previous article.

    We have leveraged Azure Cosmos DB in Advanced Analytics on Big Data design to get a greater insight and make data accessible to web and mobile applications.

    Azure Cosmos DB is a globally distributed, multi-model database service and provides native support for NoSQL choices. It offers turnkey global distribution across any number of Azure regions and elastically scales throughput and storage worldwide.

    We have leveraged Azure HDInsight with Kafka cluster in Real-time Analytics design to ingest the live streaming data.

    Azure HDInsight is a fully managed, full spectrum open-source analytics service for widespread open-source frameworks such as Hadoop, Kafka, Storm, Spark, R, Hive, etc.

    Summary

    The modern data warehouse designs support various types of business needs, including changes in data behavior, real time analytics on live streaming data and lambda architecture to serve multiple purposes with the source data. Also, selection of technology components is important to meet your business needs to build a flexible, high performing and scalable solution. Microsoft Azure provides a full set of technology components to build the hub for all types of data—structured, unstructured, or streaming—to develop transformative solutions like Business Intelligence and reporting, advanced analytics on big data, and real-time analytics.  

    See all articles by Anoop Kumar


    These 5 AI-driven Machine Learning Tools Can Make Enterprises More Productive | killexams.com real questions and Pass4sure dumps

    AI and ML have taken a consistent stance in varied industrial operations facilitating productivity, better decision-making and efficiency for enterprises

    October 10, 2018 5 min read

    You're reading Entrepreneur India, an international franchise of Entrepreneur Media.

    Artificial Intelligence and Machine Learning have barged into our lives without our knowledge and have now taken a permanent spot in there. Despite the scare of machines taking over human jobs, the technology has worked in human kind’s favour majorly. The innovation of AI and ML has indeed made our lives earlier and convenient.

    While the industry is at a nascent stage in India, people are aware of their presence. Every day these innovations are contributing to improving people’s way of living along with increasing their level of productivity. Enterprises are exploring distinctive tools for building intelligent machines and programs which can process data on their own without supervision.

    Cost and feasibility considerations have been the hindering factors for their adoption but their growing importance in industries can’t be neglected either. Addressing the future implications of the technology’s adoption, DHL SmarTrucking’s CEO Neeraj Bansal conveyed, “AI and ML will play a crucial role in making digital workforces productive and agile by facilitation of allocation and reallocation of resources based on real-time, changing requirements.

    From the business perspective, these technologies improve the quantity and quality of data collected, yield data-driven insights and forecasts and facilitate better decision-making. “The lack of bias in AI while evaluating performance and productivity and its ability to interpret and analyze big data to identify patterns and yield data-driven insights and forecasts is also an advantage,” Bansal added.

    Apart from ensuring greater accuracy, safety and scale, AI also improves efficiency and productivity for companies. Every small and large tech company has taken it upon themselves to develop a distinctive tool for both the personal and business use. Entrepreneur India enlists 5 AI tools which can enhance an entrepreneur’s productivity:

    1.       Microsoft Azure Machine Learning

    Considered as the next big thing in machine learning, Microsoft Azure provides a simplified platform to businesses for designing advanced analytics on the cloud using algorithms from Xbox, Bing, R or Python packages. “The data preparation tool helps in combining years of data into an organized and complete source,” quoted Rhetick Sengupta, Board President at Microsoft’s Snow Leopard Trust.

    The tool acts as a convenient platform for data scientists to build custom web services, publish APIs and charge for its usage. It helps to execute machine learning experiments for building and training models focused on the construction of virtualized environments. Azure ML is known for its powerful cloud-based predictive analytics and for having a marketplace with over 25 machines learning APIs.

    2.       Google Cloud Machine Learning Engine

    Are you looking to improve the customer experience of using your smart app? Google Cloud Machine Learning Engine is your solution. The service is used by multiple enterprises across the globe to solve problems ranging from identifying clouds in satellite images, ensuring food safety and responding to customer emails four times faster.

    Cloud ML Engine offers training and prediction services to enable developers and data scientists to build superior machine learning models for production. Being a subfield of artificial intelligence, the goal of ML is to make computers learn from the data that you give them and that is exactly what the Cloud ML Engine aims to achieve.

    3.       Infosys Nia

    The knowledge based Artificial Intelligence platform utilizes the organizational knowledge along with machine learning to drive automation in the enterprise. The goal is to automate repetitive processes based on the data collected from people, processes and legacy systems to solve higher-value customer problems creatively.

    The tool helps organizations in leveraging their collective knowledge to generate deep insights and discover opportunities to optimize, simplify, and automate complex business processes ultimately liberating human from monotonous tasks, pushing them towards skilled practices and reducing the cost of maintenance for both physical and digital assets.

    4.       Wipro HOLMES

    The growing requirement of automating transactional jobs has led to the creation of many smart AI-based tools and HOLMES is one of the most efficient and comprehensive ones out there. Apart from downsizing the employee numbers and cutting down processes, the platform offers a series of cognitive services that accelerate business processes through automation.

    While it stands nowhere near IBM's Watson, a question-answering computer system that is capable of answering questions posed in natural language, it uses development of digital virtual agents, predictive systems, cognitive process automation, visual computing applications, knowledge virtualization, robotics and drones to automate service desk functions.

    5.       API.AI

    Sure machines can’t provide an emotional connection to customers but can surely guide them through certain routes. API.AI helps businesses in interacting with users using the AI-powered voice and text-based conversational interfaces, such as voice apps and chatbots built. Its inbuilt natural language understanding tools help in designing unique conversation scenarios.

    The platform initially learns from the data and examples provided by the developer but eventually learn to improve through the experience from continuous interactions with the users. It uses years of domain knowledge and natural language understanding to analyze and understand the user's intent before responding.



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