DatriseAI-first ETL

Apache Spark MicroStrategy

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

How Datrise loads Apache Spark into MicroStrategy

Datrise syncs Apache Spark's records, events, and configuration objects into MicroStrategy as warehouse tables for MicroStrategy's schema objects. Flexible or custom fields land in flattened columns, and timestamps such as created, updated, and status changes are typed as date/time dimensions.

Sync is incremental: Datrise uses incremental refresh of the warehouse tables behind attributes and metrics, so re-runs update only what changed. Date-partitioned facts. MicroStrategy maps attributes to columns, so Datrise lands stable keys and names so metrics don't break.

Ideal for large-scale enterprise reporting and governance.

Endpoints

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

MicroStrategy: Enterprise BI with dossiers, governed metrics, and mobility.

How Apache Spark entities map to MicroStrategy

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

FAQ

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

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 MicroStrategy types.

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

It runs incrementally — Datrise uses incremental refresh of the warehouse tables behind attributes and metrics.

Related pipelines

Early access

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