DatriseAI-first ETL

JSON File to Databricks SQL Warehouse Integration

AI-first ETL from JSON File into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads JSON File into Databricks SQL Warehouse

Datrise syncs JSON File's documents, nested objects, arrays, keys, file batches, and schemas into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

JSON File: Structured JSON documents from exports and drops.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How JSON File entities map to Databricks SQL Warehouse

JSON File entityDatabricks SQL Warehouse objectNotes
documentsjson_file_documentsid PK · custom fields → VARIANT or STRUCT columns
nested objectsjson_file_nested_objectsid PK · linked to json_file_documents
arraysjson_file_arraysid PK · linked to json_file_documents
keysjson_file_keysid PK · linked to json_file_documents

FAQ

How does Datrise handle JSON File's custom fields in Databricks SQL Warehouse?

Flexible values are stored as VARIANT or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Databricks SQL Warehouse types.

How does the JSON File to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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