Apache Spark to Looker Integration
AI-first ETL from Apache Spark into Looker. Governed entities, incremental sync, typed landing tables.
How Datrise loads Apache Spark into Looker
Datrise syncs Apache Spark's jobs, stages, tasks, executors, SQL queries, and application metrics into Looker as governed warehouse tables with LookML-ready naming. Flexible or custom fields land in flattened columns (nested fields expanded for modeling), and timestamps such as created, updated, and status changes are typed as date/time dimension columns.
Sync is incremental: Datrise uses incremental refresh of the underlying warehouse tables Looker explores, so re-runs update only what changed. Date-partitioned fact tables for PDT performance. Looker models live in LookML on top of SQL, so Datrise lands clean, stable column names rather than churn that would break your views.
Ideal for governed, version-controlled BI on a warehouse.
Endpoints
Apache Spark: Distributed engine for large-scale data processing.
Looker: Google Cloud BI with LookML semantic models and governed dashboards.
How Apache Spark entities map to Looker
| Apache Spark entity | Looker object | Notes |
|---|---|---|
| jobs | apache_spark_jobs | id PK · custom fields → flattened columns (nested fields expanded for modeling) |
| 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 Looker?
Flexible values are stored as flattened columns (nested fields expanded for modeling), so new fields don't require a migration; strongly-typed fields — dates, numbers, and references — are promoted to native Looker types.
How does the Apache Spark to Looker sync stay up to date?
It runs incrementally — Datrise uses incremental refresh of the underlying warehouse tables Looker explores.
Related pipelines
More destinations for Apache Spark
Early access
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