Spark acceleration without new failure modes

Data engineering teams adopting Spark for performance gains frequently introduce new, unexpected failure modes and operational overhead into their data pipelines. These 'small' issues, like retry behaviors or timeouts, escalate into major incidents weeks later, costing significant time and resources in debugging and resolution. The core problem is that current acceleration solutions optimize for speed but neglect pipeline resilience and operational stability.