Amanda Moran

<p>Part 2 of this blog series will focus on how to get DataStax Enterprise Analytics with Apache Cassandra™ and Apache Spark™, Jupyter Notebooks, and all the required Python package dependencies set up via Docker.</p>

<p><img alt="Docker Logos" data-entity-type="file" data-entity-uuid="b845977b-ddf0-4ec2-ba94-8922a9670270" src="https://www.datastax.com/sites/default/files/inline-images/alllogosDocker.png" /></p>

<h2>What Problem Are We Trying to Solve?</h2>

<p>The question of our time: "What movie should I actually see?" Wouldn't it be great if you could ask 1 million people this question? Wouldn't it be great if I could automate this process? And wouldn't it be great if I didn't have to do all the installation steps detailed out in the Part 1 blog in this series?</p>

<p>Data analytics doesn't have to be complicated and neither does the step-up!</p>

<p>To do this we can use the power of Big Data, and power of a combination of technologies: DataStax Enterprise Analytics with Apache Spark™ and Apache Cassandra™, Apache Spark™ Machine Learning Libraries, Python, Pyspark, Twitter Tweets, Twitter Developer API, Jupyter notebooks, Pandas, a Python package Pattern, and Docker!</p>

<h2>How Are We Going to Solve It?</h2>

<p>In the <a href="/blog/2018/09/when-rotten-tomatoes-isnt-enough-twitter-sentiment-analysis-dse-part-1">Part 1</a> blog entry on this topic we installed everything locally using the DSE binary tar file to install, but now we will simplify the process by utilizing Docker and a previously created image.&nbsp;</p>

<h2>How to Get Started</h2>

<h3>Requirements</h3>

<ul>
	<li>Docker</li>
	<li>Download or clone this repo: <a href="https://github.com/amandamoran/pydata">https://github.com/amandamoran/pydata</a>
	<ul>
		<li>Note: This repo also includes notebooks utlizing CSV files if you would like to get started with the notebook but do not wish to create a Twitter Dev API account.</li>
	</ul>
	</li>
</ul>

<h4>Overview</h4>

<ul>
	<li>Install Docker</li>
	<li>Download DataStax Docker Image</li>
	<li>Open Jupyter</li>
</ul>

<h4>Install Docker</h4>

<ul>
	<li>Download correct Docker Community Edition: <a href="https://store.docker.com/search?type=edition&amp;offering=community">https://store.docker.com/search?type=edition&amp;offering=community</a></li>
	<li>Create Log In to Download</li>
	<li>Download Docker</li>
</ul>

<p>&nbsp;</p>

<p><img alt="Download Docker" data-entity-type="file" data-entity-uuid="7dbb79a5-610b-4e86-adf3-1b3c8432a9a0" src="https://www.datastax.com/sites/default/files/inline-images/downloaddocker_0.png" /></p>

<h3>Configure Docker Memory Settings</h3>

<ul>
	<li>Allow for 5 GB of Memory per container</li>
	<li>Docker -&gt; Preferences -&gt; Advanced -&gt; Memory</li>
</ul>

<p>&nbsp;</p>

<p><img alt="Docker Memory Settings" data-entity-type="file" data-entity-uuid="f3e455bf-4bbf-4057-aa6c-cb16da60ccd5" src="https://www.datastax.com/sites/default/files/inline-images/memorydocker.png" /></p>

<h3>Download DSE/Jupyter Images</h3>

<ul>
	<li>cd <strong>YourDownloadPath</strong>/pydata</li>
	<li>docker-compose up -d&nbsp;
	<ul>
		<li>This will take about 6 minutes (depending on your connection speed)</li>
		<li>This will start DataStax Enterprise which includes Apache Spark™, and Jupyter notebooks</li>
		<li>Must run in the same directory as the docker-compose.yaml file (This file is what has all the configuration and information on how to download and deploy these containers.)</li>
	</ul>
	</li>
</ul>

<h3>Open Jupyter</h3>

<ul>
	<li>Once download and start is complete</li>
	<li>Login with token that is in Jupyter logs</li>
	<li>docker logs pydata_jupyter_1</li>
</ul>

<p>&nbsp;</p>

<p><img alt="Open Jupyter" data-entity-type="file" data-entity-uuid="1ac31d4d-b609-4ee8-ae95-f86e0890a6a5" src="https://www.datastax.com/sites/default/files/inline-images/jupyterterm.png" /></p>

<ul>
	<li>Example: <a href="http://127.0.0.1:8889/?token=dcd21bc3a1c1331c6c61d51fb5a9d64c72fca7f4b2a6000e">http://127.0.0.1:8889/?token=dcd21bc3a1c1331c6c61d51fb5a9d64c72fca7f4b2a6000e</a></li>
	<li>Navigate to notebooks directory!!</li>
	<li>Open When Rotten Tomatoes isn’t Enough CSV.ipynb //If you want to be able to play with the notebook without have Twitter API installed</li>
</ul>

<p><img alt="Notebook" data-entity-type="file" data-entity-uuid="693aa4d6-f423-457c-8d72-979093630656" src="https://www.datastax.com/sites/default/files/inline-images/Screen%20Shot%202018-10-30%20at%203.22.12%20PM.png" /></p>

<h3>Congrats, you did it!</h3>

<h2>What's Next:</h2>

<p>Explore the notebook! Play with removing different stop words, change the confidence intervals! Data science is about exploring</p>

<h2>Stay Tuned for Part 3</h2>

<p>Stay tuned for the 3rd and final part of this series that will walk through each cell in the notebook!</p>

<p>Want even more information about how to deploy DSE Docker containers? Check out this excellent blog by Kathryn Erickson: <a href="https://academy.datastax.com/content/docker-tutorial">Docker Tutorial</a>.</p>


When Rotten Tomatoes Isn't Enough: Twitter Sentiment Analysis with DSE Part 2

Amanda Moran

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DataStax Astra DB Nabs Three Prestigious 2023 TrustRadius “Best of” Awards, Dominates the Vector Databases Category

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