Apache Spark to Redash Integration

How Datrise loads Apache Spark into Redash

Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into Redash as SQL tables Redash queries and visualizes. Flexible or custom fields land in flattened columns for query results, and timestamps such as created, updated, and status changes are typed as temporal columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts for scheduled queries. Redash caches query results on a schedule, so Datrise keeps tables incrementally fresh so cached dashboards reflect reality.

Ideal for lightweight, query-driven dashboards.

Endpoints

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

Redash: Open-source SQL client for queries, visualizations, and dashboards.

How Apache Spark entities map to Redash

Apache Spark entity	Redash object	Notes
jobs	`apache_spark_jobs`	id PK · custom fields → flattened columns for query results
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 Redash?

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

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

It runs incrementally — Datrise uses incremental refresh of the connected tables.

Related pipelines

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