Spotify Project

The technical process of the project begins with normalizing the dataset, which was originally in a disorganized and denormalized format. This initial format includes duplications and lacks relational structure, making it difficult to analyze. Normalization involves dividing the data into multiple related tables, creating separate structures for information such as artists, albums, and tracks, and establishing unique identifiers to link them.
This step reduces redundancy and ensures the database is efficient and consistent.

Next, an exploratory analysis is conducted to better understand the dataset's characteristics. This analysis helps identify patterns, relationships between variables, and potential anomalies, such as outliers or missing values. It also helps define the key questions to be answered during the analysis.

Subsequently, SQL queries of varying complexity are created. The simplest queries focus on extracting basic information, such as listing all albums by an artist or counting the total number of tracks. Intermediate queries involve more detailed analyses, such as calculating average track durations by genre or identifying the artists with the highest number of albums.
Advanced queries tackle more complex scenarios, combining data from multiple tables to generate detailed reports and answer strategic questions, such as identifying the most popular artists based on the average popularity of their songs.

Finally, query performance optimization is carried out to ensure they are fast and efficient. Queries are analyzed using tools like EXPLAIN to identify performance bottlenecks, and strategies such as creating indexes, rewriting queries, and using views or subqueries are applied to simplify complex calculations.
This approach ensures that the analysis is robust and scalable.