Dremio → Azure Data Lake Storage
AI-first ETL from Dremio into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.
How Datrise loads Dremio into Azure Data Lake Storage
Datrise syncs Dremio's records, events, and configuration objects 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
Dremio: SaaS or API data source for analytics and warehouse sync.
Azure Data Lake Storage: ADLS Gen2 object storage for analytics workloads.
How Dremio entities map to Azure Data Lake Storage
| Dremio entity | Azure Data Lake Storage object | Notes |
|---|---|---|
| records | dremio_records | id PK · custom fields → nested struct/map fields in Parquet |
| events | dremio_events | ISO-8601 timestamp columns events |
| configuration objects | dremio_configuration_objects | id PK · linked to dremio_records |
FAQ
How does Datrise handle Dremio'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 Dremio 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.
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Early access
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