Google Analytics → Azure Data Lake Storage
AI-first ETL from Google Analytics into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Analytics into Azure Data Lake Storage
Datrise syncs Google Analytics's sessions, events, channels, conversions, and behavior cohorts into Azure Data Lake Storage as partitioned Parquet in ADLS Gen2 per source entity. Flexible or custom fields land in nested struct/map fields in Parquet, and timestamps such as created, updated, and status changes are typed as ISO-8601 timestamp columns.
Sync is incremental: Datrise uses writes new date partitions to the container and compacts on a schedule, so re-runs update only what changed. Hive-style partitioning by load date, readable by Synapse and Databricks. ADLS hierarchical namespace makes folder layout matter, so Datrise keeps a predictable entity/date path your Azure engines mount directly.
Ideal for Azure lakehouse storage shared across Synapse and Databricks.
Endpoints
Google Analytics: Web and product analytics for behavior and traffic insights.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Google Analytics entities map to Azure Data Lake Storage
| Google Analytics entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| sessions | google_analytics_sessions | id PK · custom fields → nested struct/map fields in Parquet |
| events | google_analytics_events | ISO-8601 timestamp columns events |
| channels | google_analytics_channels | id PK · linked to google_analytics_sessions |
| conversions | google_analytics_conversions | id PK · linked to google_analytics_sessions |
FAQ
How does Datrise handle Google Analytics's custom fields in Azure Data Lake Storage?
Flexible values are stored as nested struct/map fields in Parquet, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Azure Data Lake Storage types.
How does the Google Analytics to Azure Data Lake Storage sync stay up to date?
It runs incrementally — Datrise uses writes new date partitions to the container and compacts on a schedule.
Related pipelines
More destinations for Google Analytics
- Google Analytics → Azure Synapse
- Google Analytics → Spreadsheets
- Google Analytics → Airtable
- Google Analytics → CSV Files
- Google Analytics → MongoDB
- Google Analytics → Supabase
- Google Analytics → Neon
- Google Analytics → PlanetScale
- Google Analytics → Amazon DynamoDB
- Google Analytics → Looker
- Google Analytics → Looker Studio
- Google Analytics → Microsoft Power BI
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- Pipeliner CRM → Azure Data Lake Storage
Early access
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