The reliability and performance of machine learning (ML) systems in production dependcritically on data engineering decisions made throughout the pipeline lifecycle. This compre-hensive technical review synthesizes ndings from 434 peer-reviewed publications spanning20182026 to quantify how upstream data collection, mid-stream preprocessing and featureengineering, and downstream versioning and monitoring decisions impact ML outcomes.We examine production systems across cybersecurity, healthcare, nance, and cloud-nativeplatforms, analyzing technical frameworks including Apache Kafka, Kubeow, MLow, andemerging feature stores. Our analysis reveals that data quality issues account for 6080% ofML system failures in production, with data engineering decisions inuencing model accu-racy by up to 40 percentage points. We identify critical decision points across the pipeline,quantify their impacts through empirical evidence, and provide actionable frameworks forpractitioners. Key ndings include: (1) streaming architectures reduce latency by 10100Öwhile maintaining accuracy within 25% of batch systems; (2) automated data validationcatches 7090% of quality issues before model training; (3) feature stores reduce feature engi-neering time by 5070% while improving consistency; and (4) comprehensive lineage trackingenables 35Ö faster debugging of production failures. This review establishes data-centricAI as essential for reliable ML systems and identies critical gaps in cost-benet analysis,cross-domain generalization, and standardized impact metrics.