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Oliver Samuel
Oliver Samuel

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A Beginner’s Guide to Big Data Analytics with Apache Spark and PySpark

#beginners #datascience #tutorial #python

Introduction

Big data is no longer a buzzword. With organizations generating terabytes of data every day, traditional tools like Pandas quickly run out of steam. Enter Apache Spark: a powerful, distributed computing engine designed for large-scale data analytics.

And with PySpark, Spark’s Python API, beginners and Pythonistas can leverage Spark’s power without switching languages.

In this hands-on article, we’ll use PySpark + SparkSQL to analyze the MovieLens dataset and uncover insights like the highest-rated movies, most active users, and most popular genres. Along the way, you’ll see how Spark handles data efficiently and why it’s a go-to tool for big data analytics.

Step 1: Start a Spark session in Jupyter Notebook:

Spark session successfully created

Step 2: Download the Dataset

We’ll use the MovieLens 100K dataset (small but rich for exploration). Download from:
👉 MovieLens Latest Small

It contains:

  • movies.csv → Movie details (id, title, genres)
  • ratings.csv → User ratings (userId, movieId, rating, timestamp)

Step 3: Load Data into Spark

First few rows of movies.csv

First few rows of ratings.csv

Step 4: Joining and Exploring the Data

Let's join the two datasets:

Average rates per movie after joining

Step 5: Analyzing with PySpark DataFrame API

Top 10 Movies by Average Rating

Top 10 movies by average rating

Plot Results

Step 6: Adding SparkSQL Queries

To make analysis more flexible, we'll use SQL:

movies.createOrReplaceTempView("movies")
ratings.createOrReplaceTempView("ratings")
movie_ratings.createOrReplaceTempView("movie_ratings")
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1. Top 10 Movies (with at least 50 ratings)

SQL output of top 10 movies with >= 50 ratings

2. Most Active Users

SQL output of most active users

3. Most Popular Genres

SQL output of most popular genres

Step 7: Visualizing Results

Let’s visualize the most popular genres using Matplotlib:

Bar chart of most popular genres

Conclusion

Big data analytics doesn’t have to be intimidating. With Apache Spark and PySpark, you can combine the power of distributed computing with the simplicity of Python.

In this article, we:

  • Loaded and explored the MovieLens dataset
  • Analyzed ratings with PySpark DataFrame API
  • Ran SparkSQL queries for deeper insights
  • Visualized results to tell a story with data

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