Movie Ratings Database Analysis (SQL)
Movie Ratings Database Analysis (SQL)
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A data storytelling project that turns relational movie data into clear, interactive insights using SQL
A data storytelling project that turns relational movie data into clear, interactive insights using SQL
This project reconstructs a relational movie-rating database from a university assignment for Advanced Topics in Database Systems and analyzes it using SQL. The system tracks interconnected data across movies, reviewers, and user-submitted scores to explore user engagement patterns, content popularity, and director benchmarks. The process includes designing schema relations, optimizing complex search queries, tracking user data collisions, and executing database maintenance operations.
This project reconstructs a relational movie-rating database from a university assignment for Advanced Topics in Database Systems and analyzes it using SQL. The system tracks interconnected data across movies, reviewers, and user-submitted scores to explore user engagement patterns, content popularity, and director benchmarks. The process includes designing schema relations, optimizing complex search queries, tracking user data collisions, and executing database maintenance operations.
The analysis simplifies complex, multi-table relationship behaviors into structured data insights, revealing patterns such as highly active platform critics, catalog performance outliers, and top-performing director assets.
The analysis simplifies complex, multi-table relationship behaviors into structured data insights, revealing patterns such as highly active platform critics, catalog performance outliers, and top-performing director assets.
The underlying database relies on three core relations:
The underlying database relies on three core relations:
- Movie: Stores catalog details including the unique ID, title, release year, and director.
- Reviewer: Records the identity and system name of platform critics.
- Rating: Tracks granular transaction data including star metrics (1–5 scale) and submission dates.
Key insights from the analysis include:
Key insights from the analysis include:
- Top-Tier Audience Favorites
Both The Shawshank Redemption and The Dark Knight tied for the absolute highest average score on the platform, holding a perfect 5.0-star rating.
Both The Shawshank Redemption and The Dark Knight tied for the absolute highest average score on the platform, holding a perfect 5.0-star rating.
- Active Reviewer Engagement
A core group of power-critics—including Sarah Martinez, Brittany Harris, and Chris Jackson—consistently drove platform engagement by contributing 3 or more ratings each.
A core group of power-critics—including Sarah Martinez, Brittany Harris, and Chris Jackson—consistently drove platform engagement by contributing 3 or more ratings each.
- Shared Interest Clusters
Multi-table self-joins exposed distinct user overlaps, highlighting pairs of different reviewers evaluating matching items across the catalog.
Multi-table self-joins exposed distinct user overlaps, highlighting pairs of different reviewers evaluating matching items across the catalog.
- Director Catalog Benchmarks
While Christopher Nolan held the most diverse footprint with multiple movies scoring a maximum of 5 stars, directors like Frank Darabont achieved peak efficiency with single, perfect-scoring assets.
While Christopher Nolan held the most diverse footprint with multiple movies scoring a maximum of 5 stars, directors like Frank Darabont achieved peak efficiency with single, perfect-scoring assets.
This project reflects my strengths in structured query optimization, database relationship design, and relational data auditing to support analytical platform decisions.
This project reflects my strengths in structured query optimization, database relationship design, and relational data auditing to support analytical platform decisions.
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