DatriseAI-first ETL

Apache Spark GoodData

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

How Datrise loads Apache Spark into GoodData

Datrise syncs Apache Spark's records, events, and configuration objects into GoodData as warehouse tables GoodData maps into its logical data model. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date dimensions.

Sync is incremental: Datrise uses incremental refresh of the connected tables, so re-runs update only what changed. Date-partitioned facts. GoodData's LDM maps datasets by keys, so Datrise lands stable primary and foreign id columns to keep the model valid.

Ideal for embedded, multi-tenant analytics.

Endpoints

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

GoodData: Composable analytics platform with headless BI and embedded dashboards.

How Apache Spark entities map to GoodData

Apache Spark entityGoodData objectNotes
recordsapache_spark_recordsid PK · custom fields → flattened columns
eventsapache_spark_eventsdate dimensions events
configuration objectsapache_spark_configuration_objectsid PK · linked to apache_spark_records

FAQ

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

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

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

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

Related pipelines

Early access

Connect Apache Spark to GoodData 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.