DatriseAI-first ETL

PostHog to Snowflake Integration

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

How Datrise loads PostHog into Snowflake

Datrise syncs PostHog's events, persons, sessions, feature flags, cohorts, and insights 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

PostHog: Product analytics with session replay and flags.

Snowflake: Cloud data warehouse with separated compute and storage.

How PostHog entities map to Snowflake

PostHog entitySnowflake objectNotes
eventsposthog_eventsTIMESTAMP_TZ events
personsposthog_personsid PK · linked to posthog_events
sessionsposthog_sessionsid PK · linked to posthog_events
feature flagsposthog_feature_flagsid PK · linked to posthog_events

FAQ

How does Datrise handle PostHog'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 PostHog 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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