About this Course
Learn to build complete data pipelines that transform raw event data into actionable insights using SQL and Pandas. You'll gain the skills to design efficient star schemas, implement Type-2 slowly changing dimensions for historical tracking, and optimize database performance for analytical workloads. This course uniquely combines hands-on experience with massive datasets (10+ million rows) and practical exposure to multiple SQL dialects including Presto and Spark. You'll benefit professionally by developing the core competencies that product analytics teams depend on daily - from data transformation and pipeline architecture to performance optimization. By completion, you'll confidently tackle real-world data engineering challenges and contribute immediately to business intelligence initiatives in product analytics roles
What You'll Learn
- Build scalable data pipelines using SQL and Pandas for raw event data transformation
- Design and optimize star schemas with slowly changing dimensions Type-2
- Implement advanced SQL window functions across different dialects like Presto and Spark
- Evaluate and enhance data warehouse performance using aggregation and indexing
Prerequisites
- Basic familiarity with relevant concepts and terminology
- Readiness to engage in practical exercises or case-based work
Instructors
Professionals from the Industry