Spark / SQL Troubleshooting with Prakhar Kumar Singh

For engineers facing a specific Spark, SQL or data pipeline problem.

Bring one anonymised example: a slow query, data skew, shuffle or memory issue, partitioning decision, Airflow failure or pipeline bottleneck. We will examine the symptoms, discuss likely causes and prioritise the next checks.

Outcome: a clearer diagnosis, the trade-offs to consider and a practical troubleshooting plan. A complete fix depends on the issue and the available context.

Before the call: share the error or query, relevant configuration and what you have already tried. Remove credentials and confidential employer or customer data.