SQL Pop Culture Datasets: Practice With Movies, Music, and Sports

Why settle for invoices and order tables when you could be querying Oscars, Spotify hits, or NBA stats? Pop culture datasets make SQL practice fun, engaging, and surprisingly effective.

Have you ever wanted to use SQL to figure out which actor has the most Oscar wins, which artist ruled the charts in the year you graduated, or which country tops the Olympic medal table? Good news: you can.

Most beginners learn SQL by analyzing customers, orders, and invoices. That’s useful, but let’s be honest—it’s not always exciting. If you want to stay motivated while learning, you need datasets that are fun and relatable. That’s where pop culture comes in. Movies, music, and sports are packed with data, and querying them is a perfect way to build real SQL skills while keeping things interesting.

If you’re just getting started—or if you need a refresh—begin with the SQL Basics course at LearnSQL.com. Our SQL courses are fully interactive: you write queries, run them on real databases, and get instant feedback. We offer tracks for beginners as well as advanced challenges, so even if you’re a seasoned data analyst you’ll find something to push your skills further. Feeling confident? Think you already know everything about SQL? Try our detailed SQL assessment and see where you really stand.

In this article, we’ll explore how you can practice SQL using pop culture datasets. We’ll look at examples from films, songs, and sports, and show you how they connect to the concepts you’ll master in our courses.

🎬 Movies: Query Your Favorite Films

Movies aren’t just entertainment—they’re data goldmines. Cast lists, release dates, budgets, box office earnings, genres, ratings… all of it fits neatly into a database.

Dataset ideas

• IMDB dataset – Maintained by IMDb, this massive dataset includes titles, cast, crew, release years, ratings, and more. It’s one of the most widely used sources for film-related data.
• The Movie Database (TMDb) – An open, community-driven database with detailed metadata on movies and TV shows. Popular for its API and up-to-date content.
• Kaggle Top 500 Popular Movies – Curated movie datasets on Kaggle, such as Top 500 Popular Movies, provide clean, ready-to-use subsets of popular films.
• Netflix shows (Kaggle) – Datasets compiled from Netflix catalogs, often scraped or shared by researchers, covering titles, genres, release years, and regions.
• Your own Netflix Wrapped– Export your personal Netflix viewing history to analyze your watch habits. Great for personalized SQL practice.
• Oscars (Kaggle) – Historical data on Academy Awards, including winners, nominees, categories, and years. Useful for queries on achievements and trends in cinema.

Practice ideas

• Which actor has appeared in the most Oscar-winning films?
• What was the highest-grossing movie in 2010?
• Which director has the highest average IMDB rating?
• How many movies in the last 20 years had budgets over $100M but failed to break even?

SQL concepts you’ll practice

• JOINs (e.g., link actors to films)
• Sorting with ORDER BY (e.g., list the top-rated films or biggest box office hits)
• Aggregates like COUNT, AVG, MAX, SUM (e.g., count films per director, find the average rating per decade, or calculate total box office by studio)

👉 This is exactly the kind of practice you’ll get in our SQL Basics course. Instead of movie stars, you’ll query structured business data—but the skills transfer directly.

🎵Music: Analyze the Charts

Music data is everywhere: charts, playlists, streaming stats, even lyrics. If you’re curious about trends, SQL can reveal fascinating insights.

Dataset ideas

• Billboard Hot 100 & more – Chart data capturing weekly rankings of top songs, with artists, release years, and performance on the charts. Perfect for exploring popularity trends over time.
• Spotify Tracks Dataset (Kaggle) – A large dataset with audio features (tempo, danceability, energy, valence), track details, artists, and popularity scores. Compiled by Kaggle contributors from Spotify’s API, it’s widely used for analyzing listening trends and music attributes.
• Make Your Own Spotify Wrapped With SQL – A hands-on guide that shows you how to export your personal Spotify history and query it with SQL. Ideal for creating your own “Wrapped”-style insights.
• Grammy Awards (Kaggle) – Covers nominees and winners from 1965 to 2024, including categories, artists, songs, and albums. Created by the Kaggle community, this dataset is popular for studying award history and artist success over decades.
• Top Classical Composers (Kaggle) – A curated dataset of classical composers with metadata such as names, eras, and countries. A simple but effective resource for analyzing music history through SQL.
• MusicNet (Kaggle) – Contains 330 classical recordings with over a million labels marking every note and instrument. Originally released for research, it’s one of the richest classical datasets, making it possible to analyze musical structure and compare composers in detail.

Practice ideas

• Who was the most-streamed artist in 2020?
• How have song lengths changed over the decades?
• Which decade produced the most Billboard #1 hits?
• Which artists consistently appear in the Top 10 year after year?
• Who holds the record for the most Grammy nominations without a win?
• Which classical composers lived the longest, and how many works are attributed to them?

SQL concepts you’ll practice

• GROUP BY and aggregate functions (e.g., count chart entries per artist or find the average song length by decade)
• Filtering with WHERE (e.g., restrict results to songs from the 1990s or only Grammy winners)
• Working with dates (e.g., compare chart performance by decade or track streaming peaks by year)

👉 If you find GROUP BY confusing, our SQL GROUP BY Practice course gives you plenty of hands-on exercises to build confidence. With a variety of real-world datasets, you’ll practice grouping and aggregating data until it becomes second nature.

🏀 Sports: Stats That Tell a Story

Sports fans know stats are part of the fun. Points, goals, medals, wins, losses—they all live in structured databases. That makes them perfect for SQL practice.

Datasets ideas

• 120 Years of Olympic History (Kaggle) – Covers Olympic athletes and results from 1896 to 2016. Includes athlete demographics, events, medals, and country stats. Widely used for historical and trend analysis.
• FIFA World Cup (GitHub) – A structured dataset of World Cup matches, teams, tournaments, goals, and outcomes. Created by sports researcher Jeffrey Fjelstul, popular for analyzing soccer history.
• NBA Dataset (Kaggle) – Comprehensive NBA data with games, teams, players, and box scores across decades. Great for comparing player careers, team success, and season-by-season performance.
• NFL – nflfastR – Public play-by-play NFL data from 1999 onward. Includes advanced stats like expected points and win probability. Data available in CSV/Parquet, widely used in sports analytics.
• Baseball – pybaseball (GitHub) – A Python library that pulls MLB data from official and semi-official sources like Baseball Savant and FanGraphs. Provides stats at game, season, and pitch level.
• Track and Field – Athletics Finals Dataset – Practice database featuring athletics finals data. Includes events, athletes, and results, designed for hands-on SQL exercises. This is part of our LearnSQL.com course SQL Practice Databases, which contains only datasets (no tasks) for free exploration. The data comes from running events at the Rio Olympics and another major athletics competition.

Practice ideas

• Which NBA player scored the most points in the 1990s?
• Who holds the record for most World Cup goals?
• Which country consistently ranks highest in Olympic gold medals?
• How has the average number of goals per match changed across tournaments?

SQL concepts you’ll practice

• Ranking queries (e.g., find top scorers or medal leaders)
• Filtering with multiple conditions (e.g., focus on specific seasons or tournaments)
• Aggregates with conditions (e.g., calculate average goals per match or points per game)

👉 Sports data is all about rankings, averages, and comparisons across seasons. That’s exactly what you’ll learn in our Creating Basic SQL Reports course — turning raw stats into clear, structured reports.

🎨 Art: Shapes of Expression

Art isn’t just about galleries and exhibitions — it’s also data waiting to be explored. Museums and researchers around the world publish structured collections with information on artists, artworks, styles, and movements. With SQL, you can reveal patterns in creativity, compare eras, and even track how certain artistic trends evolve over time.

Dataset ideas

• MoMA CollectiontHub) – The Museum of Modern Art’s open dataset, containing metadata on more than 130,000 artworks: artists, titles, mediums, dates, and classifications. Perfect for exploring modern and contemporary art.
• MoMA Practice Database (LearnSQL.com) – Our own LearnSQL.com sandbox dataset based on MoMA’s collection. It includes the same rich art metadata but is set up for hands-on SQL exploration, with no predefined tasks — just open data to query.
• Art Images (Kaggle) – A dataset of ~9,000 images of artworks across categories like drawings, paintings, sculptures, and engravings. Includes labels for type of work, useful for classification and exploration.
• Whitney Museum Open Access – Metadata on artists, artworks, and exhibitions from the Whitney Museum of American Art. Updated regularly, making it a reliable source for analyzing American art history.
• Best Artworks of All Time (Kaggle) – A curated dataset of works from 50 famous painters, including artist names, styles, and painting details. Great for comparing individual artists or studying movements across centuries.

Practice ideas

• Which artist has the largest number of works in the MoMA collection?
• How do mediums (oil, acrylic, sculpture, etc.) vary across decades?
• Which art movements are most represented in the Whitney dataset?
• Which of the “best artworks of all time” painters appear most frequently, and in which style categories?
• Compare the overlap between MoMA and Whitney — are certain artists featured in both?

SQL concepts you’ll practice

• Filtering and grouping (e.g., works by artist, medium, or decade)
• JOINs (e.g., linking artworks to exhibitions or artists)
• Aggregates like COUNT and DISTINCT (e.g., number of works per style or movement)
• Sorting and ranking (e.g., top 10 most prolific painters in a collection)

👉 Want to practice on real data? Try our SQL Practice Databases. You’ll find datasets like MoMA and Athletics Finals designed for free exploration, so you can write your own queries and uncover insights without being locked into predefined exercises.

🎮 Video Games: Play with the Data

Video games aren’t just fun to play — they also generate massive amounts of structured data. From sales numbers and reviews to esports tournaments and board game ratings, these datasets let you explore trends in entertainment, competition, and culture. With SQL, you can find out what makes a hit game, how genres evolve, or which players and teams dominate the esports scene.

Dataset ideas

• Video Game Sales (Kaggle) – Contains sales data for over 16,000 video games. Columns include name, platform, year, genre, publisher, and global/regional sales. Perfect for practicing GROUP BY, JOIN, and aggregate functions.
• Steam Games Dataset (Kaggle) – Covers Steam’s vast game catalog. Includes title, release date, developer, genre, tags, price, and reviews. Great for filtering, text search, and trend analysis.
• Esports Earnings (Kaggle) – Tournament-level data with prize pools, players, and teams. Useful for hierarchical queries, ranking, and exploring performance trends in competitive gaming.
• Board Games (Kaggle) – Data from BoardGameGeek with reviews, ratings, and categories. Excellent for practicing joins between reviews, categories, and ratings.

Practice ideas

• Which video game platform had the highest global sales in the 2000s?
• Which Steam developers have released the most games, and how do their average ratings compare?
• Who are the top 10 esports players by total earnings, and which games dominate prize pools?
• Which board game categories have the highest average ratings, and do they differ from the most-reviewed ones?
• How have game genres shifted in popularity over the past three decades?

SQL concepts you’ll practice

• Grouping and aggregating (e.g., sales by platform or genre)
• JOINs (e.g., linking reviews to board game categories)
• Ranking with ORDER BY (e.g., top-selling games or top-earning esports players)
• Filtering and text search (e.g., games with “Adventure” in their title or tags)

👉 Games are all about rankings and leaderboards — and that’s exactly where window functions shine. In our Window Functions course, you’ll learn how to rank, compare, and analyze data over time, just like tracking top players or best-selling titles.

🗂️ How to Load a CSV Dataset into a Database

Most of the datasets we’ve looked at come in CSV format. To practice SQL, you’ll usually want to load them into a database system like PostgreSQL, MySQL, or SQLite. Here’s the general process:

1. Choose your database
   • SQLite – simplest option; stores everything in a single file.
   • PostgreSQL / MySQL – more powerful, good if you want to work with larger datasets or multiple tables.
2. Create a table
   Create a table that matches the structure of your CSV file. For example, if your CSV has columns: Name, Platform, Year, Genre, Sales, your table might look like this in PostgreSQL:

CREATE TABLE videogames (
    name TEXT,
    platform TEXT,
    year INT,
    genre TEXT,
    sales NUMERIC
);

3. Import the CSV

• In PostgreSQL:

COPY videogames(name, platform, year, genre, sales)
FROM '/path/to/videogames.csv'
DELIMITER ','
CSV HEADER;

• In MySQL:

LOAD DATA INFILE '/path/to/videogames.csv'
INTO TABLE videogames
FIELDS TERMINATED BY ','
IGNORE 1 ROWS;

• In SQLite (using the command line):

sqlite3 mydatabase.db
.mode csv
.import videogames.csv videogames

4. Start querying
   Once loaded, you can run your SQL queries as usual:

SELECT genre, AVG(sales) 
FROM videogames
GROUP BY genre
ORDER BY AVG(sales) DESC;

👉 If you don’t want to deal with database setup, you can also use LearnSQL.com’s SQL Practice Databases. They’re ready to query in your browser, no installation needed.

From Fun to Professional

Practicing SQL on movies, music, sports, or games is a great way to stay motivated. But the real benefit comes when you transfer those skills to your career. Once you’ve mastered filtering, JOINs, and aggregates on fun datasets, you’ll be ready to analyze customer data, sales reports, or marketing analytics at work.

If you’d like a structured way to build these skills—without spending time hunting for datasets yourself—the All Forever SQL package is the best choice. It gives you lifetime access to every LearnSQL.com course, from the very basics to advanced topics like window functions, reporting, and subqueries. You’ll practice SQL interactively with real data, get instant feedback, and gain the confidence to query any dataset—whether it’s box office hits, Spotify charts, or your company’s database.

👉 Ready to make the jump from pop culture to professional analytics? The All Forever SQL package has everything you need to become fluent in SQL once and for all.