Module
Module 2 of 6Lesson 2 of 3~18 min

Model the data of a feature

Proposing the first data model for a feature means settling up front which uses and measurements the product will support. This lesson takes you from user stories to a logical schema you present to engineers along with your open questions. You avoid lost history and costly rework discovered too late.

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

Lesson objective

By the end of this lesson, you will be able to go from a feature's user stories to a data model (entities, typed attributes, relationships, cardinalities, keys, lifecycle) in six steps, and present it to the engineering team with the list of questions it raises.

Topics covered

  • data modeling
  • user stories
  • entities and relationships
  • data lifecycle
  • logical schema

Where it fits

Read and model data

How do my product's screens translate into tables, and how do I design the tables for my next feature?

Lessons in this module

  1. Read a data model, from screens to tables
  2. Model the data of a feature (this lesson)
  3. Relational or document database, and what it changes for you

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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