DatriseAI-first ETL

Apache Spark Supabase

AI-first ETL from Apache Spark into Supabase. Governed entities, incremental sync, typed landing tables.

How Datrise loads Apache Spark into Supabase

Datrise syncs Apache Spark's records, events, and configuration objects into Supabase as a typed table per source entity in your Supabase Postgres. Flexible or custom fields land in jsonb columns, and timestamps such as created, updated, and status changes are typed as timestamptz.

Sync is incremental: Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE, so re-runs update only what changed. Optional declarative partitioning for high-volume tables. Datrise lands into a dedicated schema and leaves row-level security to you, so synced tables don't inherit public access by accident.

Ideal for app builders who want CRM data alongside their Supabase product data.

Endpoints

Apache Spark: SaaS or API data source for analytics and warehouse sync.

Supabase: Postgres platform with auth, storage, and realtime APIs.

How Apache Spark entities map to Supabase

Apache Spark entitySupabase objectNotes
recordsapache_spark_recordsid PK · custom fields → jsonb columns
eventsapache_spark_eventstimestamptz events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

How does Datrise handle Apache Spark's custom fields in Supabase?

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

How does the Apache Spark to Supabase sync stay up to date?

It runs incrementally — Datrise uses a watermark on updated-at, applied with INSERT … ON CONFLICT DO UPDATE.

Related pipelines

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