← Apache Spark learning pathApache Spark · Module 10 of 10
Spark in production
Run, monitor and tune Spark jobs reliably.
📖 Key ideas
- Submit jobs with spark-submit or run notebooks on Databricks.
- Tune executor memory, cores and shuffle partitions.
- Monitor with Spark UI and logs.
- Schedule with Airflow or Databricks Workflows.
- Test transformations with small sample data.
💡 Real-life example: Running Spark in production is running a train service: schedules, monitoring and maintenance.
spark-submitCommand to launch Spark apps
spark.sql.shuffle.partitionsNumber of shuffle partitions
AirflowWorkflow scheduler
Spark UIWeb UI for jobs and stages