A Field Guide to Trustworthy Analytical SQL Models
Build trustworthy analytical SQL models in modern cloud warehouses.
A practical field guide for writing, testing, reviewing, and debugging analytical SQL models. Learn the grain, join, time, incrementality, testing, and review patterns that keep warehouse models from quietly breaking downstream numbers.
One-time purchase. Delivered as a ZIP through Gumroad.
Most analytical SQL bugs do not look like bugs. The query runs. The dashboard loads. The number is still wrong.
Why this guide exists
The problem is not SQL syntax. It is modeling judgment.
Many data professionals learn how to write queries before they learn how to reason about row contracts, fan-outs, historical joins, semantic grain shifts, and tests that actually catch modeling bugs.
Compilation success tells you almost nothing about whether the model preserves the intended business meaning.
A model can have the right total while assigning revenue, users, or cost to the wrong segment.
Uniqueness and non-empty checks are useful, but they rarely catch the silent bugs that damage dashboards.
Inside the guide
Read it like a book. Use it like a workbook.
The product is designed as a field guide plus practical exercise system. Each major pattern is tied to a small Ledger example dataset so you can trace the wrong output, the corrected model, and the review habit behind it.
Open START_HERE.html and read the guide comfortably in your browser, with stable navigation and copy-ready SQL blocks.
Use the included BigQuery setup script and run chapter-level exercises against the Ledger dataset.
Answer the practice questions, then compare your reasoning with the expected outputs and answer guides.
What you will learn
The failure modes that quietly break analytical models.
The guide focuses on the recurring production mistakes that make downstream metrics unreliable.
Workbook flow
Every lesson turns into a review habit.
The intended workflow is simple: read the chapter, run the query, answer the question, compare the output, and reuse the diagnostic pattern in real model reviews.
Read the chapter
Understand the modeling failure mode and the production scenario it represents.
Run the broken query
See the exact shape of the wrong output, not just a verbal warning.
Compare the fix
Use expected outputs and answer guides to understand why the fixed pattern works.
Sample idea
One innocent join can change the meaning of every row.
Chapter 2 starts with grain because almost every serious analytical modeling bug begins with a row meaning that was never made explicit.
-- BigQuery: example pattern
select
t.transaction_id,
t.amount_usd,
li.line_item_id,
li.line_item_amount_usd
from ledger_transactions t
left join ledger_transaction_line_items li
on li.transaction_id = t.transaction_id;
-- The question is not only: does this run?
-- The question is: what is one row now?Who it is for
For people who already know SQL and want stronger judgment.
This is for analysts, analytics engineers, data engineers, BI developers, and data scientists who write, review, debug, or depend on analytical SQL models.
You build warehouse models, review SQL, debug mismatched metrics, support dashboards, or want better language for explaining correctness to stakeholders.
You need a beginner SQL course, a dbt tutorial, a warehouse administration manual, or copy-paste production code for a specific company schema.
FAQ
Before you buy.
Which warehouse is this for?
The workbook uses BigQuery Standard SQL. The modeling principles apply across modern warehouses such as Snowflake, Redshift, Databricks SQL, and BigQuery.
Is this a dbt guide?
No. The guide is SQL-first and modeling-first. You can apply the ideas whether or not your team uses dbt.
How do I access it?
Gumroad delivers a ZIP. After downloading, unzip it and open START_HERE.html.
Do I get the source files?
Yes. The product includes the HTML reader, Markdown source files, SQL exercise files, expected outputs, and bonus templates.
Build models people can trust.
Get the field guide, run the workbook, and start reviewing analytical SQL models with stronger judgment around grain, joins, time, incrementality, and tests.