DatriseAI-first ETL

Zendesk Databricks SQL Warehouse

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

How Datrise loads Zendesk into Databricks SQL Warehouse

Datrise syncs Zendesk's tickets, users, organizations, macros, and satisfaction ratings 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

Zendesk: Customer support suite with tickets and knowledge base.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Zendesk entities map to Databricks SQL Warehouse

Zendesk entityDatabricks SQL Warehouse objectNotes
ticketszendesk_ticketsid PK · custom fields → VARIANT or STRUCT columns
userszendesk_usersid PK · linked to zendesk_tickets
organizationszendesk_organizationsid PK · linked to zendesk_tickets
macroszendesk_macrosid PK · linked to zendesk_tickets

FAQ

How does Datrise handle Zendesk'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 Zendesk 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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