A feature store is a centralized repository designed to manage machine learning (ML) features, which are the input variables for ML models. It transforms raw data from various sources into engineered features, serving as a single source of truth that ensures consistency between training and production environments. Feature stores address challenges such as duplicated efforts and inconsistent feature definitions by enabling reusability, standardization, and governance, thereby improving the efficiency and reliability of ML development. They support both offline and online data storage to cater to historical and real-time needs, ensuring low-latency access for live predictions critical for applications like fraud detection. Feature stores integrate batch and streaming data, maintaining the freshness and accuracy of features. Platforms like Aerospike enhance feature stores by providing high-speed, scalable, and reliable data access, which is crucial for real-time AI applications. Despite operational and integration challenges, feature stores are becoming integral to MLOps, offering significant benefits for organizations handling complex, data-rich ML operations.