---
license: cc-by-nc-4.0
task_categories:
- tabular-classification
- tabular-regression
language:
- en
tags:
- ticket-routing
- support-tickets
- servicenow
- workflow-automation
- itsm
- customer-support
- operations-analytics
- synthetic-data
- mindweave
- service-desk
- test-data
- sla
- it-service-management
- helpdesk
- incident-management
pretty_name: Help Desk Tickets (Synthetic) (Free Sample)
size_categories:
- 1K<n<10K
configs:
- config_name: agents
  data_files: data/agents.csv
  default: true
- config_name: categories
  data_files: data/categories.csv
- config_name: comments
  data_files: data/comments.csv
- config_name: tickets
  data_files: data/tickets.csv
---

# Help Desk Tickets (Synthetic) (Free Sample)

> **This is a free sample** with 3,018 rows. The full dataset has **34,253 rows** across 5 tables.

Multi-table IT service management dataset for a 500-person software and
operations company. Covers 10,000 support tickets across 18 months with
agents, categories, threaded comments, SLA tracking, and escalation logic
aligned to real service-desk workflows and priority-based response targets.

Resolution times follow realistic P1/P2/P3/P4 distributions, and the
dataset includes a major outage in month 6 that causes a 5x spike in
ticket volume. Useful for ITSM analytics, queue forecasting, workflow
automation, SLA breach reporting, and support-ops ML prototypes.


## Sample tables

| Table | Sample Rows |
|-------|------------|
| agents | 10 |
| categories | 8 |
| comments | 2,000 |
| tickets | 1,000 |
| **Total** | **3,018** |

## Full dataset

The complete dataset includes all tables with full row counts:

| Table | Full Rows |
|-------|----------|
| agents | 10 |
| categories | 8 |
| comments | 22,986 |
| sla_breaches | 1,249 |
| tickets | 10,000 |
| **Total** | **34,253** |

**Formats included:** CSV, Parquet, SQLite

**[Get the full dataset on Gumroad](https://mindweavetech.gumroad.com)**

## About

Generated by [Mindweave Technologies](https://mindweave.tech) -- realistic synthetic datasets for developers, QA teams, and data engineers.

Every dataset features:
- Enforced foreign key relationships across all tables
- Realistic statistical distributions (not uniform random)
- Temporal patterns (seasonal, time-of-day, day-of-week)
- Injected anomalies for ML training and anomaly detection
- Deterministic generation (same seed = same output)

Browse all datasets: [https://mindweavetech.gumroad.com](https://mindweavetech.gumroad.com)