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.
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What you'll learn
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
Skills you'll practice
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About this Guided Project
Learn step-by-step
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
Recommended experience
Learners should be familiar with Python programming Language, Spark Technology and have a little experience working with google colab environment
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