DatriseAI-first ETL

Apache Spark Chartio

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

How Datrise loads Apache Spark into Chartio

Datrise syncs Apache Spark's records, events, and configuration objects into Chartio as SQL tables a visual-SQL explorer connects to. Flexible or custom fields land in flattened columns for visual SQL, 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. Visual-SQL tools build joins from your schema, so Datrise lands clearly related tables with stable id columns.

Ideal for drag-and-drop charting over a database.

Endpoints

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

Chartio: Cloud BI for exploring warehouse data with drag-and-drop charts.

How Apache Spark entities map to Chartio

Apache Spark entityChartio objectNotes
recordsapache_spark_recordsid PK · custom fields → flattened columns for visual SQL
eventsapache_spark_eventstemporal columns events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

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

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

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

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

Related pipelines

Early access

Connect Apache Spark to Chartio the easy way

Skip brittle scripts and manual exports. Join the waitlist to get a guided setup, AI-assisted mapping, and reliable incremental sync for this integration.