Apache Spark → Amazon QuickSight
AI-first ETL from Apache Spark into Amazon QuickSight. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into Amazon QuickSight
Datrise syncs Apache Spark's records, events, and configuration objects into Amazon QuickSight as warehouse tables or a SPICE-loaded dataset. Flexible or custom fields land in flattened columns for analyses, and timestamps such as created, updated, and status changes are typed as date/time fields.
Sync is incremental: Datrise uses incremental refresh of the tables behind SPICE or direct query, so re-runs update only what changed. Date-partitioned facts to bound SPICE refresh. QuickSight SPICE is an in-memory copy, so Datrise keeps the backing tables incremental so refreshes stay cheap.
Ideal for AWS-native dashboards with pay-per-session pricing.
Endpoints
Apache Spark: SaaS or API data source for analytics and warehouse sync.
Amazon QuickSight: AWS serverless BI with SPICE and embedded analytics.
How Apache Spark entities map to Amazon QuickSight
| Apache Spark entity | Amazon QuickSight object | Notes |
|---|---|---|
| records | apache_spark_records | id PK · custom fields → flattened columns for analyses |
| 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 Amazon QuickSight?
Flexible values are stored as flattened columns for analyses, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Amazon QuickSight types.
How does the Apache Spark to Amazon QuickSight sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the tables behind SPICE or direct query.
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Early access
Connect Apache Spark to Amazon QuickSight the easy way
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