CoolFace
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mindweave/web-server-logs

Web Server Access Logs (Synthetic) (Free Sample) This is a free sample with 5,003 rows. The full dataset has 50,048 rows across 2 tables. Realistic HTTP access logs from a simulated SaaS company running an e-commerce API and marketing website. 50,000 requests across 3 servers over 12 months. Includes realistic patterns: weekday/weekend traffic variation, peak hours, seasonal trends, bot traffic, and two injected anomalies (DDoS attempt and database outage) for anomaly… See the full description on the dataset page: https://huggingface.co/datasets/mindweave/web-server-logs.

sourceHugging Facecc-by-nc-4.0updated 5mo agoView on Hugging Face
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Web Server Access Logs (Synthetic) (Free Sample)

This is a free sample with 5,003 rows. The full dataset has 50,048 rows across 2 tables.

Realistic HTTP access logs from a simulated SaaS company running an e-commerce API and marketing website. 50,000 requests across 3 servers over 12 months.

Includes realistic patterns: weekday/weekend traffic variation, peak hours, seasonal trends, bot traffic, and two injected anomalies (DDoS attempt and database outage) for anomaly detection training.

Each log entry includes: timestamp, server, HTTP method, path, status code, response time, bytes sent, user agent, IP address, and referrer.

Ideal for: DevOps monitoring dashboards, log analysis pipelines, anomaly detection ML models, SIEM testing, and observability tool development.

Sample tables

TableSample Rows
access_logs5,000
servers3
Total5,003

Full dataset

The complete dataset includes all tables with full row counts:

TableFull Rows
access_logs50,045
servers3
Total50,048

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