DatriseAI-first ETL

Zendesk Chat Databricks SQL Warehouse

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

How Datrise loads Zendesk Chat into Databricks SQL Warehouse

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times 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 Chat: Live chat conversations and agent performance.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How Zendesk Chat entities map to Databricks SQL Warehouse

Zendesk Chat entityDatabricks SQL Warehouse objectNotes
chatszendesk_chat_chatsid PK · custom fields → VARIANT or STRUCT columns
agentszendesk_chat_agentsid PK · linked to zendesk_chat_chats
visitorszendesk_chat_visitorsid PK · linked to zendesk_chat_chats
departmentszendesk_chat_departmentsid PK · linked to zendesk_chat_chats

FAQ

How does Datrise handle Zendesk Chat'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 Chat 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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