Overview

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.

Syllabus

ETL Pipeline Automation

In this module, you will configure automated ETL pipelines using Apache Airflow to seamlessly ingest real-time event streams from sources like Mixpanel into data warehouses such as Snowflake.

Event Data Compliance Validation

In this module, you will implement systematic validation processes to assess mobile event implementations against predefined tracking specifications, identify compliance gaps, and create actionable remediation workflows.

Create Parameterized SQL Scripts for Daily Data Materialization

You will learn to build scalable, maintainable data transformation pipelines through parameterized SQL scripting techniques.

Systematically Analyze and Optimize Query Performance

You will learn systematic performance analysis techniques to identify and resolve database bottlenecks that impact analytical workflows.

JSON Data Flattening - Foundation

You will learn systematic approaches to transform complex nested JSON structures into pandas DataFrames, enabling reliable data preprocessing for analytics pipelines.

Timezone Offset Correction - Core Application

You will develop systematic approaches to identify, diagnose, and correct timezone-related data quality issues that fragment user sessions and compromise temporal analytics.

SQL Dialect Mastery for Cross-Platform Analytics

You will learn the critical syntax variations between SQL dialects that can make or break analytical queries in enterprise data environments.

Advanced Event Data Aggregation Techniques

You will learn advanced techniques for transforming raw event streams into structured analytical datasets using both SQL and Pandas aggregation methods.

Type-2 SCD Implementation - Foundation

You will learn the fundamental concepts and practical implementation of Type-2 slowly changing dimensions to preserve complete historical data records in dimensional models.

Star Schema Evaluation & Refinement - Core Application

You will learn systematic evaluation techniques to assess star schema effectiveness and develop comprehensive refinement strategies that balance query performance, storage efficiency, and analytical capabilities.

Project: Data Pipelines and SQL for Product Analytics

You will build a complete data pipeline system that automates event data ingestion, transforms complex data structures, and creates optimized analytical datasets. This project integrates skills from automated data ingestion, SQL optimization, JSON transformation, time data correction, advanced aggregation techniques, and dimensional modeling to create a production-ready analytics infrastructure.