Datasets:

ziadatalabs
/

FreeSyntheticWebEvents50M

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

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

datasets

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

duckdb

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