Google Cloud SQL to Azure Data Lake Storage Integration
AI-first ETL from Google Cloud SQL into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Google Cloud SQL into Azure Data Lake Storage
Datrise syncs Google Cloud SQL's tables, rows, schemas, indexes, read replicas, and binlog changes 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 Cloud SQL: Managed MySQL, PostgreSQL, and SQL Server instances on GCP.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Google Cloud SQL entities map to Azure Data Lake Storage
| Google Cloud SQL entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| tables | google_cloud_sql_tables | id PK · custom fields → nested struct/map fields in Parquet |
| rows | google_cloud_sql_rows | id PK · linked to google_cloud_sql_tables |
| schemas | google_cloud_sql_schemas | id PK · linked to google_cloud_sql_tables |
| indexes | google_cloud_sql_indexes | id PK · linked to google_cloud_sql_tables |
FAQ
How does Datrise handle Google Cloud SQL'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 Cloud SQL 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 Cloud SQL
- Google Cloud SQL → Azure Synapse
- Google Cloud SQL → Spreadsheets
- Google Cloud SQL → Airtable
- Google Cloud SQL → CSV Files
- Google Cloud SQL → MongoDB
- Google Cloud SQL → Supabase
- Google Cloud SQL → Neon
- Google Cloud SQL → PlanetScale
- Google Cloud SQL → Amazon DynamoDB
- Google Cloud SQL → Looker
- Google Cloud SQL → Looker Studio
- Google Cloud SQL → Microsoft Power BI
More sources for Azure Data Lake Storage
- Google Cloud Storage → Azure Data Lake Storage
- Google Directory → Azure Data Lake Storage
- Google Ecommerce → Azure Data Lake Storage
- Google PageSpeed Insights → Azure Data Lake Storage
- Greenhouse → Azure Data Lake Storage
- Gridly → Azure Data Lake Storage
- Harness → Azure Data Lake Storage
- Harvest Forecast → Azure Data Lake Storage
- Heap → Azure Data Lake Storage
- Hellobaton → Azure Data Lake Storage
- Heroku → Azure Data Lake Storage
- PostgreSQL → Azure Data Lake Storage
Early access
Connect Google Cloud SQL to Azure Data Lake Storage the easy way
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