Each product has its own strengths and weaknesses. We need to understand the differences in order to use the right tool for the right use case. The following table shows you the differences between Jupyter and Zeppelin:

The latest Hortonworks Sandbox provides a preconfigured Zeppelin service that can be used to quickly try out. If you want to install Zepplelin on a cluster, there are a couple of ways to do so. Use the Hortonworks Ambari service or the manual installation method. The Ambari service can be used for Hortonworks-based installations and the manual installation can be used for Hortonworks, Cloudera, and MapR distributions.

VERSION=`hdp-select status hadoop-client | sed 's/hadoop-client - ([0-9].[0-9]).*/1/'`
sudo git clone https://github.com/hortonworks-gallery/ambari-zeppelin-service.git/var/lib/ambari-server/resources/stacks/HDP/$VERSION/services/ZEPPELIN
sudo ambari-server restart

wget http://mirror.metrocast.net/apache/zeppelin/zeppelin-0.6.1/zeppelin-0.6.1-bin-all.tgz

tar xzvf zeppelin-0.6.1-bin-all.tgz

cd zeppelin-0.6.1-bin-all/conf

cp /etc/hive/conf/hive-site.xml .

cp zeppelin-env.sh.template zeppelin-env.sh
cp zeppelin-site.xml.template zeppelin-site.xml

export JAVA_HOME=/usr/lib/jvm/java
export MASTER=yarn-client
export HDAOOP_CONF_DIR=/etc/hadoop/conf

<property>
  <name>zeppelin.server.addr</name>
  <value>sandbox.hortonworks.com</value>
  <description>Server address</description>
</property>

<property>
  <name>zeppelin.server.port</name>
  <value>9999</value>
  <description>Server port.</description>
</property>

cd ../bin/
./zeppelin-daemon.sh start

Zeppelin provides multiple interpreters in the same notebook. So, you can write Scala, Python, SQL, and others in the same notebook.

Click on the Notebook menu option at the top of the screen, and then click on Create new note and provide a meaningful name for the notebook. The newly created notebook can be opened from the main screen or from the Notebook menu option. Click on the newly created notebook and then on the interpreter binding button in the upper right corner. Click on the interpreters to bind or unbind the interpreters. You can change the order of interpreters by dragging and dropping them. The first one on the list will be the default interpreter in the notebook. Finally, click on the Save option at the bottom to save the changes.

Now, click on the Interpreter menu option at the top and then on edit to change Spark properties such as master, spark.cores.max, spark.executor.memory, and args as needed by the application. Click on Save to make changes to update and restart the interpreter with new settings. You can also restart any specific interpreter by clicking on the restart button.

You are now ready to code. As %spark is the first on the list of interpreter binding, you don't need to type %spark to write Scala code in the paragraph. However, in the paragraph, if you are writing any other code, say, PySpark, you need to type %pyspark. Provide a Markdown text in the first paragraph to provide information about the notebook. Write code in the next set of paragraphs. Finally, to visualize the result, write %sql or %table in a separate paragraph.

Write code from previous chapters or use the Zeppelin Tutorial notebook that comes along with Zeppelin for a quick start. You can use the following code to analyze Ambari agent logs:

%pyspark
words = sc.textFile('file:///var/log/ambari-agent/ambari-agent.log') 
 .flatMap(lambda x: x.lower().split(' ')) 
 .filter(lambda x: x.isalpha()).map(lambda x: (x, 1)) 
 .reduceByKey(lambda a,b: a+b)
 sqlContext.registerDataFrameAsTable(sqlContext.createDataFrame(words, ['word', 'count']), 'words')

%sql select word, max(count) from words group by word

The output of the preceding code looks similar to Figure 6.4:

Figure 6.4: Zeppelin visualizations

If you get any errors, check the logs in the logs directory of Zeppelin.

Hortonworks Gallery has prebuilt notebooks at https://github.com/hortonworks-gallery/zeppelin-notebooks to play with Spark, PySpark, Spark SQL, Spark Streaming, Hive, and so on.

Any existing notebook can be viewed at the ZeppelinHub Viewer:

https://www.zeppelinhub.com/viewer

There are multiple ways to share a notebook with others. Other users on the same cluster can access and run the notebook with the URL of the notebook. You can also share the notebook in report mode by clicking on the drop-down list in the upper right corner and then choosing report.