Batch and Stream Processing Questions

Covers design and implementation of data processing using batch, stream, or hybrid approaches. Candidates should be able to explain when to choose batch versus streaming based on latency, throughput, cost, data volume, and business requirements, and compare architectural patterns such as lambda and kappa. Core stream concepts include event time versus processing time, windowing strategies such as tumbling sliding and session windows, watermarks and late arrivals, event ordering and out of order data handling, stateful versus stateless processing, state management and checkpointing, and delivery semantics including exactly once and at least once. Also includes knowledge of streaming and batch engines and runtimes, connector patterns for sources and sinks, partitioning and scaling strategies, backpressure and flow control, idempotency and deduplication techniques, testing and replayability, monitoring and alerting, and integration with storage layers such as data lakes and data warehouses. Interview focus is on reasoning about correctness latency cost and operational complexity and on concrete architecture and tooling choices.