SQL Projects
SQL Skills
- Writing complex JOINs (INNER, LEFT, RIGHT, FULL OUTER) to combine data across multiple tables
- Using WHERE, GROUP BY, and HAVING clauses to filter and aggregate data
- Creating subqueries and CTEs (Common Table Expressions) for modular queries
- Applying window functions (ROW_NUMBER, RANK, LEAD, LAG) for advanced calculations
- Data transformation using CASE, COALESCE(), and string/date functions
- Calculating KPIs using aggregates: SUM, AVG, COUNT, MIN, MAX
- Using nested queries and derived tables for advanced logic
- Applying DATE/TIME functions (DATEDIFF, DATE_TRUNC, EXTRACT) for time-series analysis
- Query optimisation using indexes and performance tuning best practices
- Creating and managing views for reusable business logic
- Performing ETL-style transformations within SQL workflows
- Understanding normalisation and denormalisation for analytics use cases
- Building dashboard-ready queries for tools like Power BI and Tableau
- Working with SQL dialects: MySQL, Snowflake SQL
This page showcases a SQL project where I performed data cleaning, transformation, and revenue analysis using Spotify datasets.
- Tools: SQL Server, PostgreSQL
- Key Topics: Joins, CTEs, Window Functions, Aggregates
- Dataset: Spotify listening data and financial transactions
- Goal: Improve artist royalty insights and identify top-performing regions