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SQL Learning Lab

A hands-on SQL portfolio project focused on using relational data to investigate realistic SaaS support scenarios.

This repository documents my progression from foundational SQL queries into support-focused investigations involving ticket triage, customer impact, and resolution performance.

Skills Demonstrated

  • SELECT and column selection
  • WHERE filtering with AND / OR
  • LIKE, BETWEEN, IS NULL, and DISTINCT
  • ORDER BY and LIMIT
  • CASE expressions
  • Aggregate functions: COUNT, MIN, MAX, AVG, ROUND
  • GROUP BY and HAVING
  • Primary and foreign key relationships
  • JOIN and LEFT JOIN
  • Support-focused data investigation

Simulated Support Data

The exercises and investigations use a simple SaaS support model built around two related tables:

users

  • user_id
  • name
  • email

tickets

  • ticket_id
  • user_id
  • issue_type
  • priority
  • status
  • created_at
  • resolution_minutes

users.user_id represents the primary key for users, while tickets.user_id acts as the foreign key connecting support tickets to the users who submitted them.

Repository Structure

00-setup.sql

Creates the sample users and tickets tables and inserts simulated SaaS support records so the queries in this repository can be run against a consistent dataset.

01-basics

Foundational SQL exercises covering querying, filtering, aggregation, and relational data.

  • 01-select-basics.sql - basic data retrieval
  • 02-query-practice.sql - filtering, sorting, aliases, ranges, and CASE
  • 03-aggregate-practice.sql - aggregate functions, GROUP BY, and HAVING
  • 04-join-practice.sql - JOIN, LEFT JOIN, and key relationships

02-support-investigations

Practical investigations designed around common SaaS support questions.

01 - Ticket Triage

Investigates the support queue to:

  • identify open high-priority tickets
  • prioritize recent open tickets
  • connect tickets to affected users
  • identify customers requiring attention
  • find users with no ticket history

02 - Customer Impact

Analyzes support activity to:

  • identify customers submitting repeated tickets
  • find common issue types
  • compare average resolution times
  • identify issue categories with slower resolutions
  • surface customers with multiple open cases

03 - Resolution Analysis

Examines ticket resolution performance to:

  • identify slow resolutions
  • analyze resolution-time ranges
  • categorize tickets by resolution speed
  • connect slow high-priority tickets to affected customers

Purpose

This project demonstrates practical foundational SQL for technical support and application support work: understanding relational data, investigating customer issues, identifying patterns, and retrieving useful information from support records.