Apache Druid serves as a pivotal database solution for machine learning workflows, addressing the rising challenges posed by the exponential growth of real-time data. Its architecture, optimized for speed, scale, and streaming data, supports various stages of the machine learning pipeline, from data exploration and feature engineering to real-time inference and model accuracy monitoring. Unlike traditional databases reliant on batch processing, Druid is adept at handling vast amounts of streaming data, making it suitable for applications such as fraud detection and recommendation engines. With subsecond query responses and support for complex analytics, Druid facilitates swift data discovery and exploration, crucial for preparing and refining training datasets. It also enables rapid retrieval and storage of pre-computed inferences, supporting time-sensitive decision-making in diverse sectors, including finance and retail. Companies like DBS, Sift, and Ibotta leverage Druid for its efficiency in processing large datasets and its ability to backfill time-based data, enhancing the evaluation and accuracy of machine learning models.