Incremental loading in data pipelines offers a more modular and manageable alternative to traditional nightly batch loads, especially when working with star schemas. Switching from batch to event-driven processing enables near real-time analytics that can be scaled and parallelized. Idempotency is highlighted as a critical property for pipeline reliability, ensuring that re-running a pipeline with the same logic and time interval always produces the same result. Combining event-driven incremental loading with idempotency can eliminate the Lambda Architecture, leaving a single data flow for both batch and streaming — a pattern well-suited to Delta Lake and Spark Structured Streaming with micro-batching.