Reports, explorations, the Google Analytics Data API, and BigQuery Export display data in somewhat different ways. Use the table to compare what data is available where and to understand the limitations of each method of viewing it.
| Data availability & limitations | Reports, Insights, & Explorations | Google Analytics Data API | BigQuery |
|---|---|---|---|
| Access methodology | Google Analytics interface | Any-third party application that can access Google Analytics data on user's behalf | GCP Console or any reporting application that can query BigQuery data |
| Data scope |
Aggregated, or event and user-level data. For each request, Google chooses the table that provides the most accurate results using the default sampling settings. |
Aggregated, or event and user-level data. For each request, Google chooses the table that provides the most accurate results using the default sampling settings. |
Event and user-level data (excluding any value additions that Google Analytics makes to the data found in standard reports and explorations) |
| High cardinality1 |
Possible. When Google uses aggregate data and a report or exploration surfaces more rows than the table's row limit, an (other) row may appear. |
Possible. When Google uses aggregate data and a report or exploration surfaces more rows than the table's row limit, an (other) row may appear. |
No |
| Sampling2 | Possible. When Google uses more granular event and user-level data and a report or exploration must process more events than the quota limit, Analytics uses a representative sample of the available data. | Possible. When Google uses more granular event and user-level data and a report or exploration must process more events than the quota limit, Analytics uses a representative sample of the available data. | No |
| Data driven attribution4 | Yes | Yes | No |
| Conversion modeling5 | Included | Included | Not included |
| Behavioral modeling6 |
Included in the reporting module, including realtime Partially included in the explore module (only in path, funnel, and free-form tables) |
Included | Not included. BigQuery data contains cookieless pings collected by Google Analytics when consent mode is enabled and each session has a different user_pseudo_id. Modeling may lead to differences between standard reports and granular data in BigQuery, such as fewer active users in reports than BigQuery as modeling tries to predict multiple sessions from an individual user who declined cookies. |
| Limitations |
150 custom reports per property 200 individual explorations per user per property and up to 500 shared explorations per property can be created. Up to 10 segments per exploration can be imported. |
Analytics APIs are subject to API quotas. Analytics 360 properties have higher limits for data collection, reporting, retention and quotas. | Standard properties have a daily export limit of 1M events/day. Analytics 360 properties have a nearly limitless export. |
1 High cardinality: High-cardinality dimensions are dimensions with more than 500 unique values in one day. High-cardinality dimensions increase the number of rows in a report or exploration, making it more likely that a report or exploration hits its row limit, causing any data past the limit to be condensed into the (other) row. Learn more about high cardinality and the (other) row.
2 Sampling: Data sampling is used when the number of events returned by an exploration exceeds the limit for your property type. This allows you to still explore your data with a high level of detail by using a representative sample of your data. Learn more about sampling.
3 Data driven attribution: Data-driven attribution distributes credit for the key event based on data for each key event. Learn more about data driven attribution.
4 Conversion modeling: Conversion modeling allows for accurate conversion attribution without identifying users (for example, due to user privacy, technical limitations, or when users move between devices). Learn more about key event modeling.
5 Behavioral modeling: Behavioral modeling for consent mode uses machine learning to model the behavior of users who decline analytics cookies based on the behavior of similar users who accept analytics cookies. Learn more about behavioral modeling.