Module
Module 6 of 6Lesson 3 of 3~20 min

Final workshop: your feature's data model and your ten queries

The final workshop applies the course to your own product: the data model for one feature and a set of queries on your real questions. A self-assessment rubric helps you judge whether each piece would hold up to review by an engineer or an analyst. You leave with a reusable kit and a plan to become more self-sufficient over the coming weeks.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will have produced, on your own product, the data model of a feature and ten commented SQL queries (definition, query, check), self-assessed them with a rubric, and you will leave with an exit kit and a 7-day and 30-day application plan.

Topics covered

  • SQL workshop
  • data model
  • commented SQL queries
  • self-assessment rubric
  • exit kit

Where it fits

Query safely, with AI

How do I access real data without risk to production or to privacy, and how do I have an AI write SQL without getting it wrong?

Lessons in this module

  1. Access real data without risk, for the product and for people
  2. Have an AI write SQL, and check it
  3. Final workshop: your feature's data model and your ten queries (this lesson)

What you will learn in the course

This lesson is part of the course Model your product’s data and query it with SQL

  • Read a data model (tables, primary and foreign keys, cardinalities, entity-relationship diagram) and connect it to the product's screens and rules.
  • Model the data of a feature (entities, attributes, relationships, keys, cardinalities) and get the model validated by the engineering team.
  • Compare a relational database and a document (NoSQL) database for a product need and justify the choice based on expected queries, consistency and change.
  • Write queries that filter, sort and aggregate (SELECT, WHERE, ORDER BY, GROUP BY, HAVING) while handling NULL values and dates correctly.
  • Combine several tables with INNER and LEFT joins, and check that the result neither loses nor duplicates any row.
  • Turn a product question into a verifiable query, using CTEs and simple window functions (ROW_NUMBER, LAG, SUM OVER).
  • Query data read-only while protecting personal data, and have an AI draft SQL while checking every query before using its result.

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