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README.md

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# EMR, Spark, & Jupyter
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In this tutorial, I'm going to setup a data environment with Amazon EMR, Apache Spark, and Jupyter Notebook. Apache Spark has gotten extremely popular for big data processing and machine learning and EMR makes it incredibly simple to provision a Spark Cluster in minutes! At Mozilla we frequently spin up Spark clusters to perform data analysis and we have a [repository](github.com/mozilla/emr-bootstrap-spark) for scripts for provisioning our clusters. The scripts contained in my repository extract the functionality that is specific to creating a simple Spark cluster and installing Jupyter Notebook on the main node of the cluster.
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In this tutorial, I'm going to setup a data environment with Amazon EMR, Apache Spark, and Jupyter Notebook. Apache Spark has gotten extremely popular for big data processing and machine learning and EMR makes it incredibly simple to provision a Spark Cluster in minutes! At Mozilla we frequently spin up Spark clusters to perform data analysis and we have a [repository](https://github.com/mozilla/emr-bootstrap-spark) for scripts for provisioning our clusters. The scripts contained in my repository extract the functionality that is specific to creating a simple Spark cluster and installing Jupyter Notebook on the main node of the cluster.
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## Assumptions
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The major assumption that I make in the following tutorial is that your AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY are accessible to `awscli`. This can be solved by placing the following environmental variables in the environment file of your respective shell. There might be other solutions to this problem, but I personally use this solution.

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