DatriseAI-first ETL

Zenloop to Databricks SQL Warehouse Integration

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

How Datrise loads Zenloop into Databricks SQL Warehouse

Datrise syncs Zenloop's surveys, answers, NPS scores, respondents, sentiment labels, and feedback topics 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

Zenloop: NPS surveys and customer feedback analysis.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Zenloop entities map to Databricks SQL Warehouse

Zenloop entityDatabricks SQL Warehouse objectNotes
surveyszenloop_surveysid PK · custom fields → VARIANT or STRUCT columns
answerszenloop_answersid PK · linked to zenloop_surveys
NPS scoreszenloop_nps_scoresid PK · linked to zenloop_surveys
respondentszenloop_respondentsid PK · linked to zenloop_surveys

FAQ

How does Datrise handle Zenloop'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 Zenloop 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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