The exponential growth of e-commerce has necessitated the development of robust real-time data pipelines capable of processing high-velocity transactional data for instant decision-making. This review paper systematically examines existing methodologies, tools, and challenges in designing and implementing such pipelines, focusing on ingestion, processing, and analytics layers. By analyzing recent literature, we evaluate architectural frameworks (e.g., Kafka, Spark, Flink), performance trade-offs (latency vs. throughput), and emerging trends (AutoML, event-driven architectures). The paper highlights gaps in scalability, privacy, and interpretability while proposing future directions for intelligent, self-optimizing pipelines.