DatriseAI-first ETL

Amazon S3 to Databricks SQL Warehouse Integration

AI-first ETL from Amazon S3 into Databricks SQL Warehouse. Governed entities, incremental sync, typed landing tables.

How Datrise loads Amazon S3 into Databricks SQL Warehouse

Datrise syncs Amazon S3's buckets, objects, prefixes, object metadata, versions, and storage classes 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

Amazon S3: Object storage for files, exports, and data lakes.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Amazon S3 entities map to Databricks SQL Warehouse

Amazon S3 entityDatabricks SQL Warehouse objectNotes
bucketss3_bucketsid PK · custom fields → VARIANT or STRUCT columns
objectss3_objectsid PK · linked to s3_buckets
prefixess3_prefixesid PK · linked to s3_buckets
object metadatas3_object_metadataid PK · linked to s3_buckets

FAQ

How does Datrise handle Amazon S3'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 Amazon S3 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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