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Harshdj18/help-desk-tickets

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… See the full description on the dataset page: https://huggingface.co/datasets/Harshdj18/help-desk-tickets.

sourceHugging Facecc-by-nc-4.0updated 1mo agoView on Hugging Face
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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

TableSample Rows
agents10
categories8
comments2,000
tickets1,000
Total3,018

Full dataset

The complete dataset includes all tables with full row counts:

TableFull Rows
agents10
categories8
comments22,986
sla_breaches1,249
tickets10,000
Total34,253

Formats included: CSV, Parquet, SQLite

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

About

Generated by Mindweave Technologies -- 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