Heroku to Snowflake Integration

How Datrise loads Heroku into Snowflake

Datrise syncs Heroku's apps, dynos, releases, add-ons, config vars, and build logs 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

Heroku: Application platform with dynos and add-ons.

Snowflake: Cloud data warehouse with separated compute and storage.

How Heroku entities map to Snowflake

Heroku entity	Snowflake object	Notes
apps	`heroku_apps`	id PK · custom fields → VARIANT columns
dynos	`heroku_dynos`	id PK · linked to heroku_apps
releases	`heroku_releases`	id PK · linked to heroku_apps
add-ons	`heroku_add_ons`	id PK · linked to heroku_apps

FAQ

How does Datrise handle Heroku'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 Heroku 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

Early access

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