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+<!DOCTYPE html><html><head><title>Machine Learning Analytics with 
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
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>$PIO_HOME/conf/pio-env.sh</code> must also be properly set for the 
<code>pio export</code> command to write to HDFS instead of the local 
filesystem.</p></div> <p><h2 id='export-events-to-apache-parquet' 
class='header-anchors'>Export Events to Apache Parquet</h2><p>PredictionIO 
supports exporting your events to <a 
href="http://parquet.incubator.apache.org/";>Apache Parquet</a>, a columnar 
storage format that allows you to query quickly.</p><p>Let&#39;s export the 
data we imported in <a 
href="/templates/recommendation/quickstart/#import-sample-data">Recommendation 
Engine Template Quick Start</a>, and assume the App ID is 1.</p><div 
class="highlight shell"><table style="border-spacing: 0"><tbo
 dy><tr><td class="gutter gl" style="text-align: right"><pre 
class="lineno">1</pre></td><td class="code"><pre><span class="gp">$ 
</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
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11</pre></td><td class="code"><pre>root
+ |-- 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
+3
+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
+3
+4
+5
+6
+7
+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">$SPARK_HOME</span>/sbin/start-thriftserver.sh
+</pre></td></tr></tbody></table> </div> <p>You can test the server using the 
included Beeline client.</p><div class="highlight shell"><table 
style="border-spacing: 0"><tbody><tr><td class="gutter gl" style="text-align: 
right"><pre class="lineno">1
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20</pre></td><td class="code"><pre><span class="gp">$ </span><span 
class="nv">$SPARK_HOME</span>/bin/beeline
+<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 c
 lass="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 
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class="level-2"><a class="final" 
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class="level-2"><a class="final" 
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class="level-2"><a class="final" 
href="/datacollection/batchimport/"><span>Importing Data in 
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href="#"><span>Choosing an Algorithm(s)</span></a><ul><li class="level-2"><a 
class="final" href="/algorithm/"><span>Built-in Algo
 rithm Libraries</span></a></li><li class="level-2"><a class="final" 
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Explained</span></a></li><li class="level-3"><a c
 lass="final" 
href="/templates/recommendation/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
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Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
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 class="level-3"><a class="final" 
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with Rate Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/ecommercerecommendation/adjust-score/"><span>Adjust 
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href="#"><span>Similar Product</span></a><ul><li class="level-3"><a 
class="final" href="/templates/similarproduct/quickstart/"><span>Quick 
Start</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/dase/"><span>DASE</span></a></li><li 
class="level-3"><a
  class="final" 
href="/templates/similarproduct/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/similarproduct/multi-events-multi-algos/"><span>Multiple 
Events and Multiple Algorithms</span></a></li><li class="level-3"><a 
class="final" 
href="/templates/similarproduct/return-item-properties/"><span>Returns Item 
Properties</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/train-with-rate-event/"><span>Train with Rate 
Event</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/rid-user-set-event/"><span>Get Rid of Events 
for Users</span></a></li><li class="level-3"><a class="final" 
href="/templates/similarproduct/recommended-user/"><span>Recommend 
Users</span></a></li></ul></li><li class="level-2"><a class="expandible" 
href="#"><span>Classification</span></a><ul><li class="level-3"><a 
class="final" href="/templates/classification/quickstart/"><span>Quick 
Start</span></a></li><li cl
 ass="level-3"><a class="final" 
href="/templates/classification/dase/"><span>DASE</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/how-to/"><span>How-To</span></a></li><li 
class="level-3"><a class="final" 
href="/templates/classification/add-algorithm/"><span>Use Alternative 
Algorithm</span></a></li><li class="level-3"><a class="final" 
href="/templates/classification/reading-custom-properties/"><span>Read Custom 
Properties</span></a></li></ul></li></ul></li><li class="level-1"><a 
class="expandible" href="#"><span>Engine Template Gallery</span></a><ul><li 
class="level-2"><a class="final" 
href="/gallery/template-gallery/"><span>Browse</span></a></li><li 
class="level-2"><a class="final" 
href="/community/submit-template/"><span>Submit your Engine as a 
Template</span></a></li></ul></li><li class="level-1"><a class="expandible" 
href="#"><span>Demo Tutorials</span></a><ul><li class="level-2"><a 
class="final" href="/demo/tapster/"><span>Comics Recommendation
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 class="col-md-9 col-sm-12"><div class="content-header hidden-md 
hidden-lg"><div id="page-title"><h1>Machine Learning Analytics with 
Zeppelin</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="#building-zeppelin-for-apache-spark-1-2">Building Zeppelin for Apache 
Spark 1.2+</a> </li> <li> <a href="#preparing-zeppelin">Preparing Zeppelin</a> 
</li> <li> <a hr
 ef="#performing-analysis-with-zeppelin">Performing Analysis with Zeppelin</a> 
</li> </ul> </aside><hr/><a id="edit-page-link" 
href="https://github.com/apache/incubator-predictionio/tree/livedoc/docs/manual/source/datacollection/analytics-zeppelin.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 Zeppelin</h1></div></div><div class="content"> <p><a 
href="http://zeppelin-project.org/";>Apache Zeppelin</a> is an interactive 
computational environment built on Apache Spark like the IPython Notebook. With 
<a href="http://predictionio.incubator.apache.org";>Apache PredictionIO 
(incubating)</a> and <a href="https://spark.apache.org/sql/";>Spark SQL</a>, you 
can easily analyze your collected events when you are developing or tuning your 
engine.</p><h2 id='prerequisites' 
class='header-anchors'>Prerequisites</h2><p>The following instructions assume 
that yo
 u have the command <code>sbt</code> accessible in your shell&#39;s search 
path. Alternatively, you can use the <code>sbt</code> command that comes with 
Apache PredictionIO (incubating) at <code>$PIO_HOME/sbt/sbt</code>.</p> <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/mo
 vies --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
+2
+3
+4
+5
+6
+7
+8
+9
+10
+11</pre></td><td class="code"><pre>root
+ |-- 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='building-zeppelin-for-apache-spark-1.2+' class='header-anchors'>Building 
Zeppelin for Apache Spark 1.2+</h2><p>Start by cloning Zeppelin.</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>git clone 
https://github.com/apache/incubator-zeppelin.git
+</pre></td></tr></tbody></table> </div> <p>Build Zeppelin with Hadoop 2.4 and 
Spark 1.2 profiles.</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</pre></td><td class="code"><pre><span class="gp">$ </span><span 
class="nb">cd </span>zeppelin
+<span class="gp">$ </span>mvn clean package -Pspark-1.2 -Dhadoop.version<span 
class="o">=</span>2.4.0 -Phadoop-2.4 -DskipTests
+</pre></td></tr></tbody></table> </div> <p>Now you should have working 
Zeppelin binaries.</p><h2 id='preparing-zeppelin' 
class='header-anchors'>Preparing Zeppelin</h2><p>First, start Zeppelin.</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>bin/zeppelin-daemon.sh start
+</pre></td></tr></tbody></table> </div> <p>By default, you should be able to 
access Zeppelin via web browser at <a 
href="http://localhost:8080";>http://localhost:8080</a>. Create a new notebook 
and put the following in the first cell.</p><div class="highlight scala"><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="n">sqlc</span><span class="o">.</span><span 
class="n">parquetFile</span><span class="o">(</span><span 
class="s">"/tmp/movies"</span><span class="o">).</span><span 
class="n">registerTempTable</span><span class="o">(</span><span 
class="s">"events"</span><span class="o">)</span>
+</pre></td></tr></tbody></table> </div> <p><img alt="Preparing Zeppelin" 
src="/images/datacollection/zeppelin-01-1c476081.png"/></p><h2 
id='performing-analysis-with-zeppelin' class='header-anchors'>Performing 
Analysis with Zeppelin</h2><p>If all steps above ran successfully, you should 
have a ready-to-use analytics environment by now. Let&#39;s try a few examples 
to see if everything is functional.</p><p>In the second cell, put in this piece 
of code and run it.</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
+3</pre></td><td class="code"><pre>%sql
+SELECT entityType, event, targetEntityType, COUNT<span class="o">(</span><span 
class="k">*</span><span class="o">)</span> AS c FROM events
+GROUP BY entityType, event, targetEntityType
+</pre></td></tr></tbody></table> </div> <p><img alt="Summary of Events" 
src="/images/datacollection/zeppelin-02-b3687a33.png"/></p><p>We can also 
easily plot a pie chart.</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</pre></td><td class="code"><pre>%sql
+SELECT event, COUNT<span class="o">(</span><span class="k">*</span><span 
class="o">)</span> AS c FROM events GROUP BY event
+</pre></td></tr></tbody></table> </div> <p><img alt="Summary of Event in Pie 
Chart" src="/images/datacollection/zeppelin-03-15dcefee.png"/></p><p>And see a 
breakdown of rating values.</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
+3</pre></td><td class="code"><pre>%sql
+SELECT properties.rating AS r, COUNT<span class="o">(</span><span 
class="k">*</span><span class="o">)</span> AS c FROM events
+WHERE properties.rating IS NOT NULL GROUP BY properties.rating ORDER BY r
+</pre></td></tr></tbody></table> </div> <p><img alt="Breakdown of Rating 
Values" src="/images/datacollection/zeppelin-04-d646c299.png"/></p><p>Happy 
analyzing!</p></div></div></div></div><footer><div class="container"><div 
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unifies data for your application from multiple channels.</p><p>Data can be 
exported to Apache parquet format with <code>pio export</code> for fast 
analysis. The following analytics tools are currently supported:</p> <ol> 
<li><p><a href="/datacollection/analytics-ipynb/">IPython Notebook</a></p></li> 
<li><p><a href="/datacollection/analytics-tableau/">Tableau</a></p></li> 
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