Learn how to setup the google colab for distributed data processing
Learn applying different queries to your dataset to extract useful Information
Learn how to visualize this information using matplotlib
One of the important topics that every data analyst should be familiar with is the distributed data processing technologies. As a data analyst, you should be able to apply different queries to your dataset to extract useful information out of it. but what if your data is so big that working with it on your local machine is not easy to be done. That is when the distributed data processing and Spark Technology will become handy. So in this project, we are going to work with pyspark module in python and we are going to use google colab environment in order to apply some queries to the dataset we have related to lastfm website which is an online music service where users can listen to different songs. This dataset is containing two csv files listening.csv and genre.csv. Also, we will learn how we can visualize our query results using matplotlib.
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Prepare the Google Colab for distributed data processing
Mounting our Google Drive into Google Colab environment
Importing first file of our Dataset (1 Gb) into pySpark dataframe
Applying some Queries to extract useful information out of our data
Importing second file of our Dataset (3 Mb) into pySpark dataframe
Joining two dataframes and prepapre it for more advanced queries
Learn visualizing our query results using matplotlib
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
This course has help equip me with a lot of experience on data analysis and i really love it. Thank you Ahmad Varasteh. A big thanks to Coursera for creating such a wonderful opportunity.
Ok, but needs a longer explanation of the functions that are used and its range of possibilites.
It would have been better if more foundations of Spark framework had been provided
Best guided project for an introduction to the PySpark
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