DatriseAI-first ETL

N8n Azure Data Lake Storage

AI-first ETL from N8n into Azure Data Lake Storage. Governed entities, incremental sync, typed landing tables.

How Datrise loads N8n into Azure Data Lake Storage

Datrise syncs N8n'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

N8n: SaaS or API data source for analytics and warehouse sync.

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

How N8n entities map to Azure Data Lake Storage

N8n entityAzure Data Lake Storage objectNotes
recordsn8n_recordsid PK · custom fields → nested struct/map fields in Parquet
eventsn8n_eventsISO-8601 timestamp columns events
configuration objectsn8n_configuration_objectsid PK · linked to n8n_records

FAQ

How does Datrise handle N8n'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 N8n 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

Early access

Connect N8n 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.