GA4 Raw Event Data Sample Dataset
This dataset contains 15,000 synthetic Google Analytics 4 event records using exact BigQuery export schema columns: event_date, event_timestamp, event_name, user_pseudo_id, platform, device.category, and geo.country. It simulates a high-traffic e-commerce property with events spanning page views, purchases, and custom conversion events across web and mobile platforms. The GA4-specific quirk is that event_timestamp is stored in microseconds (16-digit integers like 1698765432100000), not milliseconds. Data engineers who mistakenly treat these values as Unix epoch milliseconds will shift all event dates to the year 53,000+, completely destroying time-series analysis and sessionization logic. The dirty data inventory includes: 340 rows with user_pseudo_id set to null (from bot traffic or consent-denied sessions), microsecond timestamps that require division by 1,000,000 before conversion, and 2,100 event_name values containing custom event parameters encoded as nested JSON strings that break flat-file parsers. After converting timestamps to standard datetime format and handling null pseudo IDs, this yields 14,660 valid user events ready for session reconstruction. Ideal for: analytics engineering tests, dbt model validation, BigQuery migration rehearsals, and attribution modeling demos. Load this dataset into the json-to-csv tool to flatten the nested event parameter JSON strings into discrete columns.
This dummy dataset simulates a standard export from Google, containing realistic yet fully anonymized records that mirror the structure and common data quality issues found in real production environments. The CSV file includes typical problems such as inconsistent formatting, missing values, duplicate entries, and non-standardized categorical fields.
It is designed to be used as a safe sandbox for testing data cleaning workflows directly in your browser — no uploads, no server round-trips, no third-party data exposure. Whether you are validating a transformation pipeline, benchmarking a cleaning tool, or simply exploring common data quality patterns, this sample provides a representative starting point without risking any sensitive business data.
Data Schema
| Column Name | Data Type | Description |
|---|---|---|
| id | string | Unique record identifier |
| created_at | timestamp | Record creation date and time |
| status | string | Current status of the record |
Recommended Tools
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