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Analytics With PowerBI On Azure

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Analytics With PowerBI On Azure

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Show various use cases and scenarios for Hadoop (tooling) on the cloud and modern data architectures.
•New insights into Analytics and Visualization, to impact the business bottom line.
•Tooling and insights provided by non-traditional approaches to data
•Example a 360 view of the customer,
•Sentiment analysis with social media such as Twitter, traffic patterns, etc.

Show various use cases and scenarios for Hadoop (tooling) on the cloud and modern data architectures.
•New insights into Analytics and Visualization, to impact the business bottom line.
•Tooling and insights provided by non-traditional approaches to data
•Example a 360 view of the customer,
•Sentiment analysis with social media such as Twitter, traffic patterns, etc.

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Analytics With PowerBI On Azure

  1. 1. VISIONING WITH HADOOP ON THE CLOUD (AZURE) SQL Business Intelligence Group Anita Luthra Feb 28, 2017
  2. 2. VISIONING WITH HADOOP & CLOUD • This presentation will show various use cases and scenarios for how Hadoop (tooling) on the cloud and modern data architectures. • We will add new insights into Analytics and Visualization, to impact the business bottom line. • The topic will cover tooling and insights provided by non- traditional approaches to data • resulting in a 360 view of the customer, • sentiment analysis with social media such as Twitter, traffic patterns, etc.
  3. 3. ANALYTICS WITH SQL DATABASE
  4. 4. WHAT IS ANALYTICS – A QUICK DEFINITION The field of data analysis. Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes.
  5. 5. SENTIMENT ANALYSIS – USE CASES Sentiment analysis is useful for customer feedback. It is important for social media monitoring, and customer experience management. We know it can be used for market research, and survey coding, and the big business use cases that deal with lots of text and need it analyzed fast.
  6. 6. EXAMPLES OF HOW CUSTOMER ENGAGEMENT ANALYSIS IS BEING USED • To obtain 360 degree view of the Customer • To Identify the customer profile • To understand product engagement with the customer • To detect if a customer is about to leave through tools to show sentiment analysis (e.g., Power BI)
  7. 7. POWER BUSINESS INTELLIGENCE (BI) CAPABILITIES A free, cloud-based business intelligence service that is simple to use, correlates disparate data and gives full visibility of business performance. Familiar, easy-to-use interface similar to Excel with the ability to create and import simple visuals. No way to publish reports and associated data together with Power BI Desktop; software is under development, so functions like data sharing are under development The Azure SQL server is where data is supported. Data for sentiment analysis, etc. is available for multiple sources such as Excel, URLs, Twitter, among others
  8. 8. VISUALIZE SENTIMENT DATA – 360 VIEW OF THE CUSTOMER To view the data you need to refine it, visualize it. The type of data is going to be a non- traditional source . We are going to look at Twitter data. Sentiment analysis – You can recognize strong sentiments, definite yes/no data. Sometimes sentiment is more gray, and like human interactions, harder to identify.
  9. 9. USE CASE 2 – SENTIMENT ANALYSIS USE CASES Tracking Flyer Experience  A large airline company started monitoring tweets about their flights to see how clients were feeling about delays, upgrades, new planes, in-flight entertainment. When they began feeding this information to their customer support platform (ZenDesk, I think?) and solving them in real-time.  One memorable instance occurred when a customer tweeted negatively about lost luggage before boarding his connecting flight. The airline caught the tweet, forwarded it to support, who got a representative to wait at the passenger’s destination, and offered him a free first class upgrade on the way back.  They also tracked the bag, and gave him information on where the bag was, and where they would deliver it the moment he stepped off the plane. The customer was excited, and tweeted happily throughout the rest of his trip.
  10. 10. USE CASE 3 – DREAM ANALYSIS University researchers analyzed dreams. The researchers accumulated ~50,000 dream transcriptions and summaries, and analyzed them with sentiment analysis to determine how people felt about certain products. The researchers began noticing crazy correlations between the things we watch during the day, and how they affect us at night.
  11. 11. USE CASE 4 – BIAS DETECTION  The winning team from the Viafora Big Data Hackathon used sentiment analysis provided by Semantria to build an open-source Chrome plugin that tracks author biases. When you run it, it checks the author name of whateverarticle you’re currently reading. It then pulls up and analyzes other works written by the same author, and pulls out his or her opinion trends using opinion mining & sentimentanalysis.  E.g., you’re reading the Huffington Post, and there’s a piece by John Doe that’s bashing the latest new Apple product. Click the plugin, all articles by John Doe appear on the side, with keywords and sentiment scores extracted. If you notice that iPhone, iPad, and Macbook are all negative, you’ll know that John has a tendency to hate on Apple products.  Really think about it. Chances are, there are probably a ton of applications that apply directly to whateverit is you’re doing on a daily basis.
  12. 12. HDFS WITH MICROSOFT ANALYTICS TOOLS  Familiar business intelligence (BI) tools - such as Excel, PowerPivot, SQL Server Analysis Services, and SQL Server Reporting Services - retrieve, analyze, and report data integrated with HDInsight by using either the Power Query add-in or the Microsoft Hive ODBC Driver. BI tools to help in your big-data analysis- Power BI on your desktop Other tools: 1. Microsoft Cloud Platform: Learn about Power BI for Office 365, download the SQL Server trial, and set up SharePoint Server 2013 and SQL Server BI. 2. Connect Excel to Hadoop with Power Query: Connect Excel to the Azure Storage account that stores the data associated with your HDInsight cluster by using Microsoft Power Query for Excel. Windows workstation required. Works with clusters on Linux or Windows. 3. Connect Excel to Hadoop with the Microsoft Hive ODBC Driver: Learn how to import data from HDInsight with the Microsoft Hive ODBC Driver. Windows workstation required. Works with clusters on Linux or Windows. 4. SQL Server Analysis Services. 5. SQL Server Reporting Services.
  13. 13. ANALYTICS TOOLS  Azure HDInsight has built-inanalytics tools to run against MapReduce and outputs of other files. One such tool is PowerBI.
  14. 14. USE CASE: HOW TO VISUALIZE SENTIMENT DATA (TWITTER/URL FEED) ON SQL DB ON AZURE, AND POWER BI
  15. 15. BUILD OUT  BIOnTwitter.doc
  16. 16. CAMPAIGN AND BRAND MANAGEMENT Analyze all your unstructured Twitter data with Microsoft’s scalable and extensible solution template. Track sentiment, topic trends and outreach for crucial occasions like events and campaigns, Or continuously monitor user reactions to your products and services. Compare current sentiment and volume to historical data to see how trends change over time.
  17. 17. SOCIAL ANALYTICS FOR YOUR TWITTER DATA 1. The Twitter template provides a complete brand/campaign solution. 2. We will stand up an end-to-end solution that pulls data from Twitter, enriches the data using machine learning and stores it in Azure SQL. 3. You can then leverage pre-built reports using Microsoft research technology to start analyzing your Twitter data and augmenting it additional data sources.
  18. 18. CAMPAIGN/BRAND MANAGEMENT FOR TWITTER 1. Get started with pre-built data models for social analytics reporting 2. Use an intuitive wizard based UI to define your Twitter search queries and spin up the workflow 3. Leverage sophisticated visualizations such as the network graph created by Microsoft Research 4. Gain near real time analytics with the process kicked off on the appearance of a new Tweet
  19. 19. WELCOME PAGE
  20. 20. SENTIMENT ANALYSIS GRAPHS
  21. 21. TROUBLESHOOTING TIP - BI  When trying to access a SQL server,after entering the server name the "Access a SQL ServerDatabase" window comes up. Select "Database" on the left and then enter the credentials. What I had tried to use was the "Windows" section with"Use alternate credentials“
  22. 22. SENTIMENT ANALYSIS – PAGE LOADING
  23. 23. SENTIMENT ANALYSIS
  24. 24. APPENDIX
  25. 25. USE CASE: HOW TO REFINE & VISUALIZE SENTIMENT DATA  Microsoft Azure (cloud)  Hortonworks Sandbox  Apache NiFi  Solr + LucidWorks HDP Search  Hive and Ambari Views  Apache Zeppelin  TwitterAPI
  26. 26. BUILD OUT OF THE HDP 2.5 SANDBOX ON AZURE Prerequisites A Microsoft Azure account. You can sign up for an evaluation account if you do not already have one. Outline Find Hortonworks Sandbox on Azure Marketplace Create the Sandbox Set a Static IP Connecting to the Sandbox Splash Screen
  27. 27. PRE-REQUISITES AND STEPS Pre-Requisites  Set up a (Microsoft Cloud) Azure account and Install the Hortonworks Sandbox with HDP  Learning the Ropes of the Hortonworks Sandbox  Deploying Hortonworks Sandbox on Microsoft Azure Outline  Install Apache NiFi  Configure and Start Solr  Create a Twitter Application  Create a Data Flow with Nifi  (Optional) Generate Random Twitter Data  Analyze and Search Data with Solr  Analyze Tweet Data in Hive  VisualizeSentiment with Zeppelin
  28. 28. THE USE CASE – WITH A HORTONWORKS BUILD-OUT  Build out a Hortonworks Sandbox (2.5 is needed)  Install Apache NiFi on your Hortonworks Sandbox if you do not have it pre-installed already. Using NiFi, we create a data flow to pull tweets directly from the Twitter API.  We will use Solr and the LucidWorks HDP Search to view our streamed data in real time to gather insights as the data arrives in our Hadoop cluster.  Next, we will use Hive to analyze the social sentiment after we have finished collecting our data from NiFi.  Finally, we will use Apache Zeppelin to create charts, so we can visualize our data directly inside of our Hadoop cluster.
  29. 29. DATA FACTORY USE CASES FOR PIG Iterative processing (sentiment analysis)  Iterative Processing is usually one very large data set that is maintained. Typical processing on that data set involves bringing in small new pieces of data that will change the state of the large data set.  E.g., consider a data set that contained all the news stories currently known to Yahoo! News. Envision this as a huge graph, where each story is a node. In this graph, there are links between stories that reference the same events. Every few minutes a new set of stories comes in, and the tools need to add these to the graph, find related stories and create links, and remove old stories that these new stories supersede.  There is constant inflow of small changes. These require the use of an incremental processing model to process this data in a reasonable amount of time.
  30. 30. DATA FACTORY USE CASES FOR PIG Iterative processing E.g., if the process has already done a join against the graph of all news stories, and a small set of new stories arrives, re-running the join across the whole set will not be desirable. It will take hours or days. Instead, joining against the new incremental data and using the results together with the results from the previous full join is the correct approach. This will take only a few minutes. Standard database operations can be implemented in this incremental way in Pig Latin, making Pig a good tool for this use case.

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