Source connector
Google Analytics connector
Google Analytics integration for databases, warehouses and BI tools
Web and product analytics for behavior and traffic insights. Datrise captures Google Analytics entities such as sessions, events, channels, conversions, and behavior cohorts, then lands normalized and typed models for warehouse reporting, attribution, and AI workflows.
The Google Analytics Data API (GA4) v1beta authenticates with OAuth 2.0, service account, incremental runs key on dateRanges (startDate/endDate on runReport) — no row-level cursor exists, and Google Analytics caps calls at 200,000 tokens per property per day and 40,000 tokens per property per hour on a standard property (2,000,000/day and 400,000/hour on Analytics 360); 10 concurrent requests per property (50 on 360).
Of the 5 objects, 3 sync as upserts keyed on the primary key and 2 as append-only streams, so each incremental run moves only what changed since the last watermark. Every object lands in its own table (for example `google_analytics_sessions`) with a schema derived from the Google Analytics API rather than a flattened CSV, in any of the 43 supported destinations.
Datrise is in early access: pick a destination below to open the Google Analytics pipeline page, and join the waitlist to get the Google Analytics connector (Product analytics) with guided onboarding.
What Google Analytics data Datrise extracts
Each object lands in its own typed table. Sync mode is derived from the object: history-style objects append, everything else upserts.
| Object | Landing table | Sync mode |
|---|---|---|
| sessions | google_analytics_sessions | Append-only, incremental by timestamp |
| events | google_analytics_events | Append-only, incremental by timestamp |
| channels | google_analytics_channels | Upsert by primary key, incremental on updated-at |
| conversions | google_analytics_conversions | Upsert by primary key, incremental on updated-at |
| behavior cohorts | google_analytics_behavior_cohorts | Upsert by primary key, incremental on updated-at |
Google Analytics API: setup facts for the connector
Verified against the vendor documentation on 2026-09-02. Unknown values are left blank rather than guessed.
| API | Google Analytics Data API (GA4) v1beta |
|---|---|
| Authentication | OAuth 2.0, service account — OAuth 2.0 or a service account granted at least Viewer access on the GA4 property, using the analytics.readonly scope; Universal Analytics' older Reporting API is fully shut down. |
| Pagination | offset — runReport takes limit/offset in the request body; default limit is 10,000 rows and the documented maximum is 250,000 rows per request. |
| Rate limit | 200,000 tokens per property per day and 40,000 tokens per property per hour on a standard property (2,000,000/day and 400,000/hour on Analytics 360); 10 concurrent requests per property (50 on 360) |
| Incremental cursor | dateRanges (startDate/endDate on runReport) — no row-level cursor exists — GA4 has no changes feed; practice is a rolling date-range re-pull because Google continues to process and reconcile hits for days after collection. |
| Webhooks | No |
| Bulk export | No |
| Deletes detectable | No |
What bites when replicating Google Analytics
- Core reporting quota is 200,000 tokens per property per day and 40,000 tokens per property per hour (10x higher on Analytics 360); most simple requests cost 10 tokens or fewer, but wide date ranges and many dimensions cost more.
- Only 10 concurrent requests per property are allowed (50 on 360); parallelizing many report calls at once trips a 429 well before the daily token quota is exhausted.
- Reports can be sampled once a property's event volume crosses Google's internal thresholds; check `metadata.samplingMetadatas` on the `runReport` response (samplesReadCount vs samplingSpaceSize) — its absence means the pull was unsampled.
- There is no incremental changes feed, so extraction relies on re-pulling a rolling `dateRanges` window; a hard append-only cutover will silently miss GA4's late corrections to already-collected days.
Popular destinations for Google Analytics
Choose a destination below to open the ready pipeline page.
- AAirtable
Relational spreadsheet destination for ops and go-to-market teams.
View pipeline - AAmazon Athena
Serverless SQL over S3 data lake tables.
View pipeline - AAmazon DynamoDB
Serverless key-value and document store on AWS.
View pipeline - AAmazon QuickSight
AWS serverless BI with SPICE and embedded analytics.
View pipeline - AAmazon Redshift
AWS petabyte-scale warehouse with Spectrum.
View pipeline - AAmazon S3 Data Lake
Object storage landing zone for parquet and snapshots.
View pipeline - AApache Superset
Open-source BI for SQL exploration, charts, and dashboard publishing.
View pipeline - AAzure Data Lake Storage
ADLS Gen2 object storage for analytics workloads.
View pipeline - AAzure Synapse
Microsoft analytics workspace with SQL pools.
View pipeline
Google Analytics connector FAQ
- What Google Analytics data does the connector extract?
- 5 objects: sessions, events, channels, conversions and behavior cohorts. Each lands in its own table with types preserved; history-style objects (2: sessions and events) load as append-only streams.
- Is the Google Analytics integration incremental?
- Yes. After the initial full load, every run reads only records changed since the last watermark (updated-at or event timestamp) and applies an upsert or an append depending on the object.
- Where can I load Google Analytics data?
- Into 43 destinations: relational databases, cloud warehouses, data lakes, spreadsheets and BI tools. Each Google Analytics → destination pair has its own page with the object-to-table mapping.
Related Product analytics sources
Other sources in the same category, each with its own connector page.
Early access
Launch your Google Analytics pipeline with guided onboarding
Join the waitlist to get priority access for Google Analytics integrations with guided setup, schema-aware mapping, and production-grade incremental sync.