README.md

---
license: cc-by-nc-4.0
task_categories:
- time-series-forecasting
- tabular-classification
language:
- en
tags:
- synthetic-data
- web-analytics
- event-stream
- clickstream
- time-series
size_categories:
- 10M<n<100M
---

# FreeSyntheticWebEvents50M

A free dataset of 50 million fully synthetic web analytics events, built for developers and researchers who need realistic clickstream and event-stream data at scale — for testing analytics pipelines, funnel and conversion analysis, session-based recommendation, anomaly detection, or time-series and streaming tooling. No real people, sessions, or sites are represented in this data.

## Schema

| Column | Type | Description |
|---|---|---|
| event_id | string | Unique event identifier |
| session_id | string | Synthetic session identifier |
| user_id | string | Synthetic user identifier |
| event_type | string | Event type (page_view, click, add_to_cart, purchase, etc.) |
| page_url | string | Page path the event occurred on |
| device_type | string | mobile, desktop, or tablet |
| event_timestamp | string | Event time (YYYY-MM-DD HH:MM:SS) |
| session_duration_sec | int | Session duration in seconds, skewed |

## Format

Single Parquet file, Snappy compression, ~1.6 GB, 50,000,000 rows.

## Quick Start

**pandas**
```python
import pandas as pd
df = pd.read_parquet("events_50M.parquet")
```

**datasets**
```python
from datasets import load_dataset
ds = load_dataset("ziadatalabs/FreeSyntheticWebEvents50M")
```

**duckdb**
```python
import duckdb
duckdb.sql("SELECT * FROM 'events_50M.parquet' LIMIT 10").show()
```

## Notes

Event timestamps follow realistic temporal patterns — busier during daytime and weekdays, sparse overnight — rather than uniform-random times, so the data is usable for time-series and anomaly-detection work. Event types follow a realistic funnel (frequent page views and clicks, rare purchases), and device split reflects typical web traffic. All entirely synthetic.

## License & Usage

Released under CC BY-NC 4.0 — personal, research, and educational use permitted, attribution required, no commercial use.

---

Created by Zia Data Labs. Questions or feedback: zia.data.team@protonmail.com