DatriseAI-first ETL

Apache Spark Looker Studio

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

How Datrise loads Apache Spark into Looker Studio

Datrise syncs Apache Spark's records, events, and configuration objects into Looker Studio as warehouse tables Looker Studio connects to. Flexible or custom fields land in flattened columns for chart fields, and timestamps such as created, updated, and status changes are typed as date dimension columns.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned tables to keep extract refresh fast. Looker Studio performs best on pre-aggregated tables, so Datrise lands tidy, report-shaped tables rather than raw API payloads.

Ideal for free, shareable dashboards on Google data sources.

Endpoints

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

Looker Studio: Google self-service dashboards and reporting (formerly Data Studio).

How Apache Spark entities map to Looker Studio

Apache Spark entityLooker Studio objectNotes
recordsapache_spark_recordsid PK · custom fields → flattened columns for chart fields
eventsapache_spark_eventsdate dimension columns events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

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

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

How does the Apache Spark to Looker Studio sync stay up to date?

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

Related pipelines

Early access

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