DatriseAI-first ETL

Zendesk Chat DuckDB

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

How Datrise loads Zendesk Chat into DuckDB

Datrise syncs Zendesk Chat's chats, agents, visitors, departments, and response times into DuckDB as a typed table per source entity in a DuckDB file. Flexible or custom fields land in JSON or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP WITH TIME ZONE.

Sync is incremental: Datrise uses rewrites changed entities into the local database (or Parquet) on each run, so re-runs update only what changed. Hive-partitioned Parquet by load date when exporting. DuckDB is single-writer and embedded, so Datrise produces a consistent file snapshot rather than concurrent streaming writes.

Ideal for local and notebook analytics without standing up a server.

Endpoints

Zendesk Chat: Live chat conversations and agent performance.

DuckDB: In-process analytics database for fast local OLAP.

How Zendesk Chat entities map to DuckDB

Zendesk Chat entityDuckDB objectNotes
chatszendesk_chat_chatsid PK · custom fields → JSON 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 DuckDB?

Flexible values are stored as JSON or STRUCT columns, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native DuckDB types.

How does the Zendesk Chat to DuckDB sync stay up to date?

It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.

Related pipelines

Early access

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