DatriseAI-first ETL

Teradata to Databricks SQL Warehouse Integration

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

How Datrise loads Teradata into Databricks SQL Warehouse

Datrise syncs Teradata's tables, rows, schemas, indexes, primary indexes, and query history 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

Teradata: Teradata analytical database for enterprise workloads.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Teradata entities map to Databricks SQL Warehouse

Teradata entityDatabricks SQL Warehouse objectNotes
tablesteradata_d_tablesid PK · custom fields → VARIANT or STRUCT columns
rowsteradata_d_rowsid PK · linked to teradata_d_tables
schemasteradata_d_schemasid PK · linked to teradata_d_tables
indexesteradata_d_indexesid PK · linked to teradata_d_tables

FAQ

How does Datrise handle Teradata'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 Teradata 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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