DatriseAI-first ETL

PostHog to Databricks SQL Warehouse Integration

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

How Datrise loads PostHog into Databricks SQL Warehouse

Datrise syncs PostHog's events, persons, sessions, feature flags, cohorts, and insights into Databricks SQL Warehouse as a Delta Lake table per source entity. Flexible or custom fields land in VARIANT or STRUCT columns, and timestamps such as created, updated, and status changes are typed as TIMESTAMP.

Sync is incremental: Datrise uses a Delta MERGE on stable id, with change history available via time travel, so re-runs update only what changed. Delta partitioning by load date with OPTIMIZE/Z-ORDER on query keys. Datrise writes Unity Catalog–governed Delta tables, so lineage and permissions are managed centrally rather than per-notebook.

Ideal for lakehouse analytics and ML feature tables on Databricks.

Endpoints

PostHog: Product analytics with session replay and flags.

Databricks SQL Warehouse: Lakehouse SQL endpoints over Delta tables.

How PostHog entities map to Databricks SQL Warehouse

PostHog entityDatabricks SQL Warehouse objectNotes
eventsposthog_eventsTIMESTAMP 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 Databricks SQL Warehouse?

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

How does the PostHog to Databricks SQL Warehouse sync stay up to date?

It runs incrementally — Datrise uses a Delta MERGE on stable id, with change history available via time travel.

Related pipelines

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