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Continously ingest and deliver both streaming and batch change data from 100s of sources using Estuary's custom no-code connectors.
- <100ms Data pipelines
- 100+ Connectors
- 2-5x less than batch ELT
Google Analytics V4 Data API connector details
The Google Analytics Data API connector syncs data from Google Analytics 4 (GA4) properties into Estuary collections using the GA4 Data API. It delivers dependable, right-time visibility into website and app engagement metrics.
- Comprehensive coverage: Capture data on active users, devices, pages, traffic sources, and locations, or define custom GAQL-style reports.
- Right-time sync cadence: Incremental updates (default: every 12 hours) keep dashboards current without exceeding API limits.
- Custom reporting: Build tailored collections with flexible dimensions, metrics, and filters, including total and max aggregations.
- Secure setup: Connect via OAuth or service credentials for automated access.
- Smart lookback window: Configure up to 30 days to capture delayed or revised GA4 data.
💡 Data freshness: Right-time polling (periodic incremental sync) — reliable GA4 reporting with flexible interval control.
HIGH THROUGHPUT
Distributed event-driven architecture enable boundless scaling with exactly-once semantics.
DURABLE REPLICATION
Cloud storage backed CDC w/ heart beats ensures reliability, even if your destination is down.
REAL-TIME INGESTION
Capture and relay every insert, update, and delete in milliseconds.
Real-timehigh throughput
Point a connector and replicate changes from Google Analytics V4 Data API in <100ms. Leverage high-availability, high-throughput Change Data Capture.Or choose from 100s of batch and real-time connectors to move and transform data using ELT and ETL.
- Ensure your Google Analytics V4 Data API insights always reflect the latest data by connecting your databases to Google Analytics V4 Data API with change data capture.
- Or connect critical SaaS apps to Google Analytics V4 Data API with real-time data pipelines.
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Pipelines as fast as Kafka, easy as managed ELT/ETL, cheaper than building it.
Feature Comparison
| Estuary | Batch ELT/ETL | DIY Python | KAFKA | |
|---|---|---|---|---|
| Price | $ | $$-$$$$ | $-$$$$ | $-$$$$ |
| Speed | <100ms | 5min+ | Varies | <100ms |
| Ease | Analysts can manage | Analysts can manage | Data Engineer | Senior Data Engineer |
| Scale |
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