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@article{195196,
        author = {Dr.K.Jeyalakshmi and Ms.K.Keerthana and Ms.R.Manisha},
        title = {End-to-End Spotify Data Analysis Pipeline},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {7641-7644},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=195196},
        abstract = {In the rapidly expanding digital music industry, massive volumes of streaming data are generated daily through user interactions, track plays, and artist engagement. Efficient analysis of this data is essential for understanding listener behavior, identifying trending tracks, and evaluating artist performance. This project presents a structured end-to-end data analytics pipeline designed to analyze Spotify data and identify top-performing tracks and artists based on popularity and streaming metrics. The system extracts data using the Spotify API, processes it through an ETL (Extract, Transform, Load) pipeline, and stores it in a relational database. SQL-based analysis and data visualization techniques are used to derive meaningful business insights. The proposed solution demonstrates how structured data analytics supports automated and data-driven decision-making in the digital music industry.},
        keywords = {Spotify Data Analysis, ETL Pipeline, SQL, Business Intelligence, Data Aggregation, API Integration.},
        month = {March},
        }