Apache Spark to Neon Integration

How Datrise loads Apache Spark into Neon

Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into Neon as a typed table per source entity. 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 by load date. Neon separates compute from storage, so Datrise batches writes to keep autoscaling compute from cold-starting on every small change.

Ideal for serverless Postgres workloads that scale to zero between syncs.

Endpoints

Apache Spark: Distributed engine for large-scale data processing.

Neon: Serverless Postgres destination with branching and autoscaling.

How Apache Spark entities map to Neon

Apache Spark entity	Neon object	Notes
jobs	`apache_spark_jobs`	id PK · custom fields → jsonb columns
stages	`apache_spark_stages`	id PK · linked to apache_spark_jobs
tasks	`apache_spark_tasks`	id PK · linked to apache_spark_jobs
executors	`apache_spark_executors`	id PK · linked to apache_spark_jobs

FAQ

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

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 Neon types.

How does the Apache Spark to Neon 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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