A collection of SQL projects demonstrating data analysis, transformation, and modeling capabilities across different databases and business domains.
Advanced SQL techniques for analyzing user behavior, tracking events, and measuring engagement patterns from large datasets in Snowflake.
Demonstrating database schema design, normalization techniques, and optimized querying approaches for complex relational data structures.
With over 5 years of SQL experience across PostgreSQL, MySQL, SQL Server, and Snowflake, I've developed expertise in data transformation, analytics, and performance optimization. The examples in this portfolio demonstrate both functional approaches for specific business problems and foundational techniques that can be applied across domains.
Snowflake, MySQL, SQL Server, PopSQL, Amazon Redshift
SQL scripts for extracting meaningful insights from user behavior data
Collection of SQL scripts that transform raw web analytics data into meaningful user engagement metrics. These queries were developed for the Google Analytics data project to help product teams understand user behavior patterns.
Using LEAD(), LAG(), ROW_NUMBER() for tracking sequential events and timing
Complex, multi-stage data processing using WITH clauses for readability and modularity
Extracting and transforming nested parameters from semi-structured event data
Designing efficient database schemas and data warehousing solutions
SQL scripts demonstrating database design principles, normalization, and optimization techniques. These examples showcase dimensional modeling for analytics and efficient relational designs for transactional systems.
CREATE TABLE statements with proper constraints, keys and index definitions
Creating and efficiently refreshing materialized views for analytics performance
Implementing table partitioning strategies for improved query performance on large tables