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SELECT * FROM FACT_DE;: A Practical guide to building reliable Data Pipelines
Think like a Data Engineer. Build pipelines that actually work.
Most people jump straight into tools—SQL, Python, Spark—without a framework. The result? Broken dashboards, rework at 2 a.m., and pipelines nobody trusts. This book takes a different mindset first, tools second.
You’ll learn the mental models and frameworks that experienced data engineers keep in their back
REQUEST for gathering requirements that won’t bite you later.
ACCUT for testing data quality (Accuracy, Correctness, Completeness, Uniqueness, Timeliness).
Kimball’s 4-step modeling method to design facts and dimensions that stand the test of time.
Medallion architecture (Bronze–Silver–Gold) explained simply, with real project context.
Then you’ll put them into
Build an end-to-end pipeline locally with Postgres, Airflow, and Superset.
Write SQL and PySpark that’s reliable and testable.
Monitor and optimize with the Spark UI and cost hygiene techniques.
Ship a working metrics layer and dashboards that can take load.
This isn’t a textbook. It’s short checklists, real configs, and war stories from production. By the end, you won’t just know what a data engineer does—you’ll have done it yourself.
Who this book is
Aspiring or junior Data Engineers who want a clear path from “what is a pipeline?” to “I shipped one.”
Software and analytics folks who need DE process to scale their work.
Interview preppers who want frameworks and practical examples to lean on.
Whether you’re aiming for your first DE role or trying to level up, this book will help you build pipelines that are a little faster, a little cleaner, and a lot more reliable.
Happy learning!
Most people jump straight into tools—SQL, Python, Spark—without a framework. The result? Broken dashboards, rework at 2 a.m., and pipelines nobody trusts. This book takes a different mindset first, tools second.
You’ll learn the mental models and frameworks that experienced data engineers keep in their back
REQUEST for gathering requirements that won’t bite you later.
ACCUT for testing data quality (Accuracy, Correctness, Completeness, Uniqueness, Timeliness).
Kimball’s 4-step modeling method to design facts and dimensions that stand the test of time.
Medallion architecture (Bronze–Silver–Gold) explained simply, with real project context.
Then you’ll put them into
Build an end-to-end pipeline locally with Postgres, Airflow, and Superset.
Write SQL and PySpark that’s reliable and testable.
Monitor and optimize with the Spark UI and cost hygiene techniques.
Ship a working metrics layer and dashboards that can take load.
This isn’t a textbook. It’s short checklists, real configs, and war stories from production. By the end, you won’t just know what a data engineer does—you’ll have done it yourself.
Who this book is
Aspiring or junior Data Engineers who want a clear path from “what is a pipeline?” to “I shipped one.”
Software and analytics folks who need DE process to scale their work.
Interview preppers who want frameworks and practical examples to lean on.
Whether you’re aiming for your first DE role or trying to level up, this book will help you build pipelines that are a little faster, a little cleaner, and a lot more reliable.
Happy learning!
377 pages, Kindle Edition
Published October 17, 2025
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