DatriseAI-first ETL

Secoda to Databricks SQL Warehouse Integration

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

How Datrise loads Secoda into Databricks SQL Warehouse

Datrise syncs Secoda's tables, columns, dictionaries, lineage edges, owners, and documentation entries 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

Secoda: Data catalog, lineage, and documentation.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Secoda entities map to Databricks SQL Warehouse

Secoda entityDatabricks SQL Warehouse objectNotes
tablessecoda_tablesid PK · custom fields → VARIANT or STRUCT columns
columnssecoda_columnsid PK · linked to secoda_tables
dictionariessecoda_dictionariesid PK · linked to secoda_tables
lineage edgessecoda_lineage_edgesid PK · linked to secoda_tables

FAQ

How does Datrise handle Secoda'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 Secoda 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

Early access

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