| --- |
| 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 |
|
|
