DatriseAI-first ETL

Public API to Snowflake Integration

AI-first ETL from Public API into Snowflake. Governed entities, incremental sync, typed landing tables.

How Datrise loads Public API into Snowflake

Datrise syncs Public API's records, response payloads, pagination cursors, request parameters, schemas, and fetch timestamps into Snowflake as a typed table per source entity. Flexible or custom fields land in VARIANT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP_TZ.

Sync is incremental: Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size, so re-runs update only what changed. Automatic micro-partitioning, with optional clustering keys on high-cardinality ids. Snowflake upper-cases unquoted identifiers, so Datrise standardizes on lower-case quoted names to keep column references stable.

Ideal for central analytics warehouses feeding BI and AI workloads.

Endpoints

Public API: Generic REST endpoint pulled on a schedule.

Snowflake: Cloud data warehouse with separated compute and storage.

How Public API entities map to Snowflake

Public API entitySnowflake objectNotes
recordspublic_api_recordsid PK · custom fields → VARIANT columns
response payloadspublic_api_response_payloadsid PK · linked to public_api_records
pagination cursorspublic_api_pagination_cursorsid PK · linked to public_api_records
request parameterspublic_api_request_parametersid PK · linked to public_api_records

FAQ

How does Datrise handle Public API's custom fields in Snowflake?

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

How does the Public API to Snowflake sync stay up to date?

It runs incrementally — Datrise uses staged loads merged on stable id with MERGE, so credits scale with change volume, not table size.

Related pipelines

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