What are some common interruptions, and what techniques have you used to optimize a price pump job?

Common interruptions in batch or streaming jobs include network latency, disk I/O bottlenecks, resource contention (CPU, memory, network), external API failures, data skew, and scheduling conflicts. To mitigate these, engineers often use partitioning to distribute data evenly, cache intermediate results to reduce recomputation, enable dynamic allocation to adjust executor resources, and implement retry logic for transient failures. Speculative execution can help when tasks are slow due to stragglers. Monitoring and alerting allow early detection of issues, while checkpointing and lineage in Spark enable fault tolerance. Additionally, choosing efficient data formats (Parquet, ORC) and tuning serialization (Kryo) can reduce I/O and memory overhead, leading to faster, more reliable job execution.