DatriseAI-first ETL

Zendesk Chat Microsoft SQL Server

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

How Datrise loads Zendesk Chat into Microsoft SQL Server

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times into Microsoft SQL Server as a typed table per source entity. Flexible or custom fields land in NVARCHAR(MAX) JSON columns, and timestamps such as created, updated, and status changes are typed as datetime2.

Sync is incremental: Datrise uses a watermark on updated-at, applied with a MERGE statement, so re-runs update only what changed. Optional partitioned tables on a date partition function. SQL Server defaults to a case-insensitive collation, so Datrise preserves original casing in a metadata column to avoid silent key collisions.

Ideal for Microsoft-stack analytics and Power BI Import models.

Endpoints

Zendesk Chat: Live chat conversations and agent performance.

Microsoft SQL Server: Microsoft relational DB with enterprise features.

How Zendesk Chat entities map to Microsoft SQL Server

Zendesk Chat entityMicrosoft SQL Server objectNotes
chatszendesk_chat_chatsid PK · custom fields → NVARCHAR(MAX) JSON 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 Microsoft SQL Server?

Flexible values are stored as NVARCHAR(MAX) JSON columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Microsoft SQL Server types.

How does the Zendesk Chat to Microsoft SQL Server sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with a MERGE statement.

Related pipelines

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