Google BigQuery to Azure Data Lake Storage Integration
AI-first ETL from Google BigQuery into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google BigQuery into Azure Data Lake Storage
Datrise syncs Google BigQuery's datasets, tables, views, query jobs, table schemas, and partitions 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 BigQuery: Serverless analytics warehouse on Google Cloud.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Google BigQuery entities map to Azure Data Lake Storage
| Google BigQuery entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| datasets | bigquery_datasets | id PK · custom fields → nested struct/map fields in Parquet |
| tables | bigquery_tables | id PK · linked to bigquery_datasets |
| views | bigquery_views | id PK · linked to bigquery_datasets |
| query jobs | bigquery_query_jobs | id PK · linked to bigquery_datasets |
FAQ
How does Datrise handle Google BigQuery'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 BigQuery 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 BigQuery
- Google BigQuery → Azure Synapse
- Google BigQuery → Spreadsheets
- Google BigQuery → Airtable
- Google BigQuery → CSV Files
- Google BigQuery → MongoDB
- Google BigQuery → Supabase
- Google BigQuery → Neon
- Google BigQuery → PlanetScale
- Google BigQuery → Amazon DynamoDB
- Google BigQuery → Looker
- Google BigQuery → Looker Studio
- Google BigQuery → Microsoft Power BI
More sources for Azure Data Lake Storage
- Branch → Azure Data Lake Storage
- Braze → Azure Data Lake Storage
- Bronto → Azure Data Lake Storage
- CallRail → Azure Data Lake Storage
- Campaign Monitor → Azure Data Lake Storage
- Cart → Azure Data Lake Storage
- ChartMogul → Azure Data Lake Storage
- CircleCI → Azure Data Lake Storage
- CleverTap → Azure Data Lake Storage
- ClickHouse → Azure Data Lake Storage
- Clockify → Azure Data Lake Storage
- Club Speed → Azure Data Lake Storage
Early access
Connect Google BigQuery to Azure Data Lake Storage the easy way
Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.