Apache Spark to ThoughtSpot Integration
AI-first ETL from Apache Spark into ThoughtSpot. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into ThoughtSpot
Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into ThoughtSpot as warehouse tables ThoughtSpot indexes for search. Flexible or custom fields land in flattened columns for searchable fields, and timestamps such as created, updated, and status changes are typed as date/time columns.
Sync is incremental: Datrise uses incremental refresh of the indexed tables, so re-runs update only what changed. Date-partitioned facts for live-query performance. ThoughtSpot search relies on clear names and relationships, so Datrise lands well-named, joinable tables.
Ideal for natural-language search analytics over a warehouse.
Endpoints
Apache Spark: Distributed engine for large-scale data processing.
ThoughtSpot: Search-driven analytics with AI-assisted insights on warehouse data.
How Apache Spark entities map to ThoughtSpot
| Apache Spark entity | ThoughtSpot object | Notes |
|---|---|---|
| jobs | apache_spark_jobs | id PK · custom fields → flattened columns for searchable fields |
| stages | apache_spark_stages | id PK · linked to apache_spark_jobs |
| tasks | apache_spark_tasks | id PK · linked to apache_spark_jobs |
| executors | apache_spark_executors | id PK · linked to apache_spark_jobs |
FAQ
How does Datrise handle Apache Spark's custom fields in ThoughtSpot?
Flexible values are stored as flattened columns for searchable fields, so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native ThoughtSpot types.
How does the Apache Spark to ThoughtSpot sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the indexed tables.
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Early access
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