<!DOCTYPE html><html><head><title>Machine Learning Analytics with Tableau</title><meta charset="utf-8"/><meta content="IE=edge,chrome=1" http-equiv="X-UA-Compatible"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/><meta class="swiftype" name="title" data-type="string" content="Machine Learning Analytics with Tableau"/><link rel="canonical" href="https://docs.prediction.io/datacollection/analytics-tableau/"/><link href="/images/favicon/normal-b330020a.png" rel="shortcut icon"/><link href="/images/favicon/apple-c0febcf2.png" rel="apple-touch-icon"/><link href="//fonts.googleapis.com/css?family=Open+Sans:300italic,400italic,600italic,700italic,800italic,400,300,600,700,800" rel="stylesheet"/><link href="//maxcdn.bootstrapcdn.com/font-awesome/4.2.0/css/font-awesome.min.css" rel="stylesheet"/><link href="/stylesheets/application-3598c7d7.css" rel="stylesheet" type="text/css"/><script 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hidden-lg"><div id="page-title"><h1>Machine Learning Analytics with Tableau</h1></div></div><div id="table-of-content-wrapper"><h5>On this page</h5><aside id="table-of-contents"><ul> <li> <a href="#prerequisites">Prerequisites</a> </li> <li> <a href="#creating-hive-tables">Creating Hive Tables</a> </li> <li> <a href="#launch-spark-sql-s-thrift-jdbc-odbc-server">Launch Spark SQL's Thrift JDBC/ODBC Server</a> </li> <li> <a href="#performing-analysis-with-tableau">Performing Analysis with Tableau</a> </li> </ul> </aside><hr/><a id="edit-page-link" href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/datacollection/analytics-tableau.html.md.erb"><img src="/images/icons/edit-pencil-d6c1bb3d.png"/>Edit this page</a></div><div class="content-header hidden-sm hidden-xs"><div id="page-title"><h1>Machine Learning Analytics with Tableau</h1></div></div><div class="content"><p>With Spark SQL, it is possible to connect Tableau to Apache PredictionIO (incubating) Event Server for interactive analysis of event data.</p><h2 id='prerequisites' class='header-anchors'>Prerequisites</h2> <ul> <li>Tableau Desktop 8.3+ with a proper license key that supports Spark SQL;</li> <li>Spark ODBC Driver from Databricks (<a href="https://databricks.com/spark/odbc-driver-download">https://databricks.com/spark/odbc-driver-download</a>);</li> <li>Apache Hadoop 2.4+</li> <li>Apache Hive 0.3.1+</li> </ul> <div class="alert-message info"><p>In this article, we will assume that you have a working HDFS, and that your environmental variable <code>HADOOP_HOME</code> has been properly set. This is essential for Apache Hive to function properly. In addition, <code>HADOOP_CONF_DIR</code> in <code>$PIO_HOME/conf/pio-env.sh</code> must also be properly set for the <code>pio export</code> command to write to HDFS instead of the local filesystem.</p></div><p><h2 id='export-events-to-apache-parquet' class='header-anchors'>Export Events to Apache Parquet</h2><p>PredictionIO supports exporting your events to <a href="http://parquet.incubator.apache.org/">Apache Parquet</a>, a columnar storage format that allows you to query quickly.</p><p>Let&#39;s export the data we imported in <a href="/templates/recommendation/quickstart/#import-sample-data">Recommendation Engine Template Quick Start</a>, and assume the App ID is 1.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$PIO_HOME</span>/bin/pio <span class="nb">export</span> --appid 1 --output /tmp/movies --format parquet
</pre></td></tr></tbody></table> </div> <p>After the command has finished successfully, you should see something similar to the following.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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 |-- creationTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- entityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- entityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- event: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- eventId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- eventTime: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- properties: struct <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |    |-- rating: double <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- targetEntityId: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
 |-- targetEntityType: string <span class="o">(</span>nullable <span class="o">=</span> <span class="nb">true</span><span class="o">)</span>
</pre></td></tr></tbody></table> </div></p><h2 id='creating-hive-tables' class='header-anchors'>Creating Hive Tables</h2><p>Before you can use Spark SQL&#39;s Thrift JDBC/ODBC Server, you will need to create the table schema in Hive first. Please make sure to replace <code>path_of_hive</code> with the real path.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
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4</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nb">cd </span>path_of_hive
<span class="gp">$ </span>bin/hive
<span class="gp">hive&gt; </span>CREATE EXTERNAL TABLE events <span class="o">(</span>event STRING, entityType STRING, entityId STRING, targetEntityType STRING, targetEntityId STRING, properties STRUCT&lt;rating:DOUBLE&gt;<span class="o">)</span> STORED AS parquet LOCATION <span class="s1">'/tmp/movies'</span>;
<span class="gp">hive&gt; </span><span class="nb">exit</span>;
</pre></td></tr></tbody></table> </div> <h2 id='launch-spark-sql&#39;s-thrift-jdbc/odbc-server' class='header-anchors'>Launch Spark SQL&#39;s Thrift JDBC/ODBC Server</h2><p>Once you have created your Hive tables, create a Hive configuration in your Spark installation. If you have a custom <code>hive-site.xml</code>, simply copy or link it to <code>$SPARK_HOME/conf</code>. Otherwise, Hive would have created a local Derby database, and you will need to let Spark knows about it. Create <code>$SPARK_HOME/conf/hive-site.xml</code> from scratch with the following template.</p><div class="alert-message warning"><p>You must change <code>/opt/apache-hive-0.13.1-bin</code> below to a real Hive path.</p></div><div class="highlight xml"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
2
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8</pre></td><td class="code"><pre><span class="cp">&lt;?xml version="1.0" encoding="UTF-8" standalone="no"?&gt;</span>
<span class="cp">&lt;?xml-stylesheet type="text/xsl" href="configuration.xsl"?&gt;</span>
<span class="nt">&lt;configuration&gt;</span>
  <span class="nt">&lt;property&gt;</span>
    <span class="nt">&lt;name&gt;</span>javax.jdo.option.ConnectionURL<span class="nt">&lt;/name&gt;</span>
    <span class="nt">&lt;value&gt;</span>jdbc:derby:;databaseName=/opt/apache-hive-0.13.1-bin/metastore_db;create=true<span class="nt">&lt;/value&gt;</span>
  <span class="nt">&lt;/property&gt;</span>
<span class="nt">&lt;/configuration&gt;</span>
</pre></td></tr></tbody></table> </div> <p>Launch Spark SQL&#39;s Thift JDBC/ODBC Server by</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ </span><span class="nv">$SPARK_HOME</span>/sbin/start-thriftserver.sh
</pre></td></tr></tbody></table> </div> <p>You can test the server using the included Beeline client.</p><div class="highlight shell"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1
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<span class="gp">beeline&gt; </span>!connect jdbc:hive2://localhost:10000
<span class="o">(</span>Use empty username and password when prompted<span class="o">)</span>
0: jdbc:hive2://localhost:10000&gt; <span class="k">select</span> <span class="k">*</span> from events limit 10;
+--------+-------------+-----------+-------------------+-----------------+------------------+
| event  | entitytype  | entityid  | targetentitytype  | targetentityid  |    properties    |
+--------+-------------+-----------+-------------------+-----------------+------------------+
| buy    | user        | 3         | item              | 0               | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| buy    | user        | 3         | item              | 1               | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| rate   | user        | 3         | item              | 2               | <span class="o">{</span><span class="s2">"rating"</span>:1.0<span class="o">}</span>   |
| buy    | user        | 3         | item              | 7               | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| buy    | user        | 3         | item              | 8               | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| buy    | user        | 3         | item              | 9               | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| rate   | user        | 3         | item              | 14              | <span class="o">{</span><span class="s2">"rating"</span>:1.0<span class="o">}</span>   |
| buy    | user        | 3         | item              | 15              | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| buy    | user        | 3         | item              | 16              | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
| buy    | user        | 3         | item              | 18              | <span class="o">{</span><span class="s2">"rating"</span>:null<span class="o">}</span>  |
+--------+-------------+-----------+-------------------+-----------------+------------------+
10 rows selected <span class="o">(</span>0.515 seconds<span class="o">)</span>
0: jdbc:hive2://localhost:10000&gt;
</pre></td></tr></tbody></table> </div> <p>Now you are ready to use Tableau!</p><h2 id='performing-analysis-with-tableau' class='header-anchors'>Performing Analysis with Tableau</h2><p>Launch Tableau and Connect to Data. Click on <strong>Spark SQL (Beta)</strong> and enter Spark SQL&#39;s Thrift JDBC/ODBC Server information. Make sure to pick <strong>User Name</strong> as <strong>Authentication</strong>. Click <strong>Connect</strong>.</p><p><img alt="Tableau and Spark SQL" src="/images/datacollection/tableau-01-b5a23839.png"/></p><p>On the next page, pick <strong>default</strong> under <strong>Schema</strong>.</p><div class="alert-message info"><p>You may not see any choices when you click on Schema. Simply press Enter and Tableau will try to list all schemas.</p></div><p>Once you see a list of tables that includes <strong>events</strong>, click <strong>New Custom SQL</strong>, then enter the following.</p><div class="highlight sql"><table style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: right"><pre class="lineno">1</pre></td><td class="code"><pre><span class="k">SELECT</span> <span class="n">event</span><span class="p">,</span> <span class="n">entityType</span><span class="p">,</span> <span class="n">entityId</span><span class="p">,</span> <span class="n">targetEntityType</span><span class="p">,</span> <span class="n">targetEntityId</span><span class="p">,</span> <span class="n">properties</span><span class="p">.</span><span class="n">rating</span> <span class="k">FROM</span> <span class="n">events</span>
</pre></td></tr></tbody></table> </div> <p>Click <strong>Update Now</strong>. You should see the following screen by now, indicating success in loading data. Using a custom SQL allows you to extract arbitrary fields from within properties.</p><p><img alt="Setting up Tableau" src="/images/datacollection/tableau-02-76e93443.png"/></p><p>Click <strong>Go to Worksheet</strong> and start analyzing. The following shows an example of breaking down different rating values.</p><p><img alt="Rating Values Breakdown" src="/images/datacollection/tableau-03-e389351e.png"/></p><p>The following shows a summary of interactions.</p><p><img alt="Interactions" src="/images/datacollection/tableau-04-c8c31bb7.png"/></p><p>Happy analyzing!</p></div></div></div></div><footer><div class="container"><div class="seperator"></div><div class="row"><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Community</h4><ul><li><a href="//docs.prediction.io/install/" target="blank">Download</a></li><li><a href="//docs.prediction.io/" target="blank">Docs</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">GitHub</a></li><li><a href="mailto:user-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to User Mailing List</a></li><li><a href="//stackoverflow.com/questions/tagged/predictionio" target="blank">Stackoverflow</a></li></ul></div></div><div class="col-md-6 col-xs-6 footer-link-column"><div class="footer-link-column-row"><h4>Contribute</h4><ul><li><a href="//predictionio.incubator.apache.org/community/contribute-code/" target="blank">Contribute</a></li><li><a href="//github.com/apache/incubator-predictionio" target="blank">Source Code</a></li><li><a href="//issues.apache.org/jira/browse/PIO" target="blank">Bug Tracker</a></li><li><a href="mailto:dev-subscribe@predictionio.incubator.apache.org" target="blank">Subscribe to Development Mailing List</a></li></ul></div></div></div></div><div id="footer-bottom"><div class="container"><div class="row"><div class="col-md-12"><div id="footer-logo-wrapper"><img alt="PredictionIO" src="/images/logos/logo-white-d1e9c6e6.png"/></div><div id="social-icons-wrapper"><a class="github-button" href="https://github.com/apache/incubator-predictionio" data-style="mega" data-count-href="/apache/incubator-predictionio/stargazers" data-count-api="/repos/apache/incubator-predictionio#stargazers_count" data-count-aria-label="# stargazers on GitHub" aria-label="Star apache/incubator-predictionio on GitHub">Star</a> <a class="github-button" href="https://github.com/apache/incubator-predictionio/fork" data-icon="octicon-git-branch" data-style="mega" data-count-href="/apache/incubator-predictionio/network" data-count-api="/repos/apache/incubator-predictionio#forks_count" data-count-aria-label="# forks on GitHub" aria-label="Fork apache/incubator-predictionio on GitHub">Fork</a> <script id="github-bjs" async="" defer="" src="https://buttons.github.io/buttons.js"></script><a href="//www.facebook.com/predictionio" target="blank"><img alt="PredictionIO on Twitter" src="/images/icons/twitter-ea9dc152.png"/></a> <a href="//twitter.com/predictionio" target="blank"><img alt="PredictionIO on Facebook" src="/images/icons/facebook-5c57939c.png"/></a> </div></div></div></div></div></footer></div><script>(function(w,d,t,u,n,s,e){w['SwiftypeObject']=n;w[n]=w[n]||function(){
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