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.
Data Pipelines and SQL for Product Analytics
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Data Pipelines and SQL for Product Analytics
This course is part of Product Analytics Unlocked: Metrics to Meaningful Insight Specialization
Instructor: Professionals from the Industry
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Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
Flexible schedule
Learn at your own pace
What you'll 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.
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Recently updated!
March 2026
Taught in English
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This course is part of the Product Analytics Unlocked: Metrics to Meaningful Insight Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
- Learn new concepts from industry experts
- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
- Earn a shareable career certificate
There are 11 modules in this course
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.
What's included
2 videos1 reading1 assignment1 ungraded lab
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.
What's included
1 video2 readings2 assignments
You will learn to build scalable, maintainable data transformation pipelines through parameterized SQL scripting techniques.
What's included
3 videos1 reading2 assignments
You will learn systematic performance analysis techniques to identify and resolve database bottlenecks that impact analytical workflows.
What's included
2 videos2 readings3 assignments
You will learn systematic approaches to transform complex nested JSON structures into pandas DataFrames, enabling reliable data preprocessing for analytics pipelines.
What's included
3 videos1 reading1 assignment
You will develop systematic approaches to identify, diagnose, and correct timezone-related data quality issues that fragment user sessions and compromise temporal analytics.
What's included
2 videos1 reading3 assignments1 ungraded lab
You will learn the critical syntax variations between SQL dialects that can make or break analytical queries in enterprise data environments.
What's included
2 videos2 readings1 assignment
You will learn advanced techniques for transforming raw event streams into structured analytical datasets using both SQL and Pandas aggregation methods.
What's included
2 videos1 reading3 assignments1 ungraded lab
You will learn the fundamental concepts and practical implementation of Type-2 slowly changing dimensions to preserve complete historical data records in dimensional models.
What's included
2 videos2 readings1 assignment
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.
What's included
2 videos2 readings3 assignments
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.
What's included
4 readings1 assignment
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