Apache Spark → Sisense
AI-first ETL from Apache Spark into Sisense. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into Sisense
Datrise syncs Apache Spark's records, events, and configuration objects into Sisense as modeled tables for a Sisense ElastiCube (or live connection). Flexible or custom fields land in flattened columns for the cube, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental ElastiCube builds on changed rows, so re-runs update only what changed. Date-partitioned facts to speed cube builds. ElastiCube is an in-memory model, so Datrise lands incremental, build-friendly tables rather than forcing full rebuilds.
Ideal for embedded analytics on an in-memory engine.
Endpoints
Apache Spark: SaaS or API data source for analytics and warehouse sync.
Sisense: Analytics platform with elastic data models and embedded analytics.
How Apache Spark entities map to Sisense
| Apache Spark entity | Sisense object | Notes |
|---|---|---|
| records | apache_spark_records | id PK · custom fields → flattened columns for the cube |
| events | apache_spark_events | date/time fields events |
| configuration objects | apache_spark_configuration_objects | id PK · linked to apache_spark_records |
FAQ
How does Datrise handle Apache Spark's custom fields in Sisense?
Flexible values are stored as flattened columns for the cube, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Sisense types.
How does the Apache Spark to Sisense sync stay up to date?
It runs incrementally — Datrise uses incremental ElastiCube builds on changed rows.
Related pipelines
More destinations for Apache Spark
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