Intercom → DuckDB
AI-first ETL from Intercom into DuckDB. Governed entities, incremental sync, typed landing tables.
How Datrise loads Intercom into DuckDB
Datrise syncs Intercom's conversations, customer attributes, inbox events, and support engagement 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
Intercom: Customer messaging platform with CRM-style account and conversation context.
DuckDB: In-process analytics database for fast local OLAP.
How Intercom entities map to DuckDB
| Intercom entity | DuckDB object | Notes |
|---|---|---|
| conversations | intercom_conversations | id PK · custom fields → JSON or STRUCT columns |
| customer attributes | intercom_customer_attributes | id PK · linked to intercom_conversations |
| inbox events | intercom_inbox_events | TIMESTAMP WITH TIME ZONE events |
| support engagement | intercom_support_engagement | id PK · linked to intercom_conversations |
FAQ
How does Datrise handle Intercom'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 Intercom to DuckDB sync stay up to date?
It runs incrementally — Datrise uses rewrites changed entities into the local database (or Parquet) on each run.
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Early access
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