Module
Module 2 of 6Lesson 1 of 3~18 min

Read a data model, from screens to tables

Reading an entity-relationship diagram lets you check that the database actually reflects the rules in your spec. Tables, keys and cardinalities turn into product statements you can hold up against the screens. You catch gaps between the model and the need before they turn into bugs or wrong numbers.

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Lesson objective

By the end of this lesson, you will be able to read a product's data model (tables, columns, types, primary and foreign keys, cardinalities) on an entity-relationship diagram, and connect each table and each link to a screen or a product rule.

Topics covered

  • entity-relationship diagram
  • primary key
  • foreign key
  • cardinality
  • data model

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 (this lesson)
  2. Model the data of a feature
  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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