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Using PySpark, SPARK and AZURE

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Have a few questions to answer, using PySpark, SPARK and AZURE ML. Need experience with these technologies, to be able to solve questions.

In Q1, you will work with a subset of the TLC dataset to get warmed up with PySpark. Apache Spark is a
framework for distributed computing, and PySpark is its Python API. You will use this tool to answer
questions such as “what are the top 10 most common trips in the dataset”? You will be using your own
machine for computation, using an environment defined by a Docker container.

In Q2, you will perform further analysis on a different subset of the TLC dataset using Spark on DataBricks,
a platform combining datasets, machine learning models, and cloud compute. This part of the assignment will
be completed in the Scala programming language, a modern general-purpose language with a robust support
for functional programming. The Spark distributed computing framework is in fact written using Scala.

In Q3, you will use PySpark on AWS using Elastic MapReduce (EMR),

and in Q4 you will use Spark on Google Cloud Platform, to analyze even larger samples from the TLC dataset.

Finally, in Q5 you will use the Microsoft Azure ML Studio to implement a regression model to predict
automobile prices using a sample dataset already included in the Azure workspace. A main goal of this
assignment is to help students gain exposure to a variety of tools that will be useful in the future (e.g., future
project, research, career). The reasoning behind intentionally including AWS, Azure and GCP (most courses
use only one), because we want students to be able to try and compare these platforms as they evolve rapidly.
This will help the students in the future should they need to select a cloud platform to use, they can make
more informed decisions and be able to get started right away.