Apache Spark → Tableau
AI-first ETL from Apache Spark into Tableau. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into Tableau
Datrise syncs Apache Spark's records, events, and configuration objects into Tableau as warehouse tables or a refreshed .hyper extract. Flexible or custom fields land in flattened columns for Tableau fields, and timestamps such as created, updated, and status changes are typed as date/datetime fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind a live connection or extract, so re-runs update only what changed. Date-partitioned facts to keep extract refresh quick. Tableau .hyper extracts snapshot data, so Datrise keeps the source tables incremental and lets you choose live vs extract.
Ideal for visual analytics and dashboards in Tableau.
Endpoints
Apache Spark: SaaS or API data source for analytics and warehouse sync.
Tableau: Salesforce analytics platform for interactive dashboards and visual exploration.
How Apache Spark entities map to Tableau
| Apache Spark entity | Tableau object | Notes |
|---|---|---|
| records | apache_spark_records | id PK · custom fields → flattened columns for Tableau fields |
| events | apache_spark_events | date/datetime 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 Tableau?
Flexible values are stored as flattened columns for Tableau fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Tableau types.
How does the Apache Spark to Tableau sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind a live connection or extract.
Related pipelines
More destinations for Apache Spark
Early access
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