The primary aim of this project is to conduct an in-depth analysis of two databases—IMDB and Magist employing advanced data analytics techniques, including stored functions and processes, CTEs (Common Table Expressions), and window functions. The overarching goal is to enhance analytical efficiency, reduce time and cost, and pave the way for future data analysts to expedite their processes.
This repository showcases my analytical prowess on two distinct databases.
- The first involves a comprehensive analysis of the IMDB database, utilizing advanced SQL tools and functions to streamline analytics processes and ensure a thorough understanding of the dataset.
- The second part involves revisiting the Magist database from Project 1 , where I not only expand the analysis but also demonstrate a full-circle application of skills, including creating new tables, integrating them into the existing database, and implementing stored functions and processes for a holistic database mastery.
- Conducted a detailed examination of the IMDB database.
- Implemented advanced SQL techniques, such as Stored Functions and Processes, CTEs, and Window Functions.
- Prioritized the creation of stored functions and processes to enhance long-term time and cost savings.
- Revisited the Magist database, building upon the analysis performed in a previous project.
- Expanded the database by creating new tables and seamlessly integrating them into the existing structure.
- Applied stored functions and processes to ensure a comprehensive and efficient analytical approach.
- Demonstrated raw data transformation for a deeper understanding of the dataset.
The project includes:
- IMDB analysis questions and corresponding SQL code.
- Magist analysis questions and corresponding SQL code.
- In-Depth Analysis: Thorough examination of datasets to derive meaningful insights.
- Stored Functions and Processes: Implementation of procedures for long-term time and cost savings.
- CTE's (Common Table Expressions): Leveraging CTEs for efficient and readable SQL queries.
- Window Functions: Employing window functions for advanced analytics.
- Raw Data Transformation: Manipulating raw data to extract valuable information.
- Database Expansion: Extending database structure through the creation of new tables and integration of analytical processes.
To enhance the project:
- Explore the possibility of incorporating additional advanced SQL features.
- Consider optimizing queries for better performance.
- Collaborate with data analysts to gather feedback for continuous improvement.
- Document and share insights on a broader platform, such as a Medium article, to contribute to the community's knowledge base.