Google Cloud Storage to Azure Data Lake Storage Integration

How Datrise loads Google Cloud Storage into Azure Data Lake Storage

Datrise syncs Google Cloud Storage's buckets, objects, prefixes, object metadata, generations, and storage classes 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 Storage: Object storage for files and data lake exports on GCP.

Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.

How Google Cloud Storage entities map to Azure Data Lake Storage

Google Cloud Storage entity	Azure Data Lake Storage object	Notes
buckets	`google_cloud_storage_f_buckets`	id PK · custom fields → nested struct/map fields in Parquet
objects	`google_cloud_storage_f_objects`	id PK · linked to google_cloud_storage_f_buckets
prefixes	`google_cloud_storage_f_prefixes`	id PK · linked to google_cloud_storage_f_buckets
object metadata	`google_cloud_storage_f_object_metadata`	id PK · linked to google_cloud_storage_f_buckets

FAQ

How does Datrise handle Google Cloud Storage'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 Storage 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 Storage

More sources for Azure Data Lake Storage

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