Data Pipelines and SQL for Product Analytics

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 will learn

• Build scalable data pipelines using SQL and Pandas to transform 10+ million rows of raw event data into structured analytics datasets.
• Design and optimize star schemas with Type-2 slowly changing dimensions to track historical changes in product analytics data.
• Compare and implement advanced SQL window functions across different dialects like Presto and Spark for cross-platform compatibility.
• Evaluate existing data warehouse schemas and propose performance refinements using aggregation techniques and indexing strategies.