The rise of the data lake is driven by the need to capture and analyze unprecedented volumes of data from digital transformation initiatives. However, exposing this data to operational applications without proper integration is a challenge, as users struggle to maximize returns on their Hadoop investments. The traditional Enterprise Data Warehouse (EDW) is overwhelmed by the sheer volume and variety of data pouring into businesses, making it difficult to store in a cost-efficient way. As a result, organizations have turned to Hadoop-based data lakes, which provide levels of performance, efficiency, and low Total Cost of Ownership (TCO) unmatched by EDWs. However, these data lakes are not designed to provide real-time access to operational applications, which need millisecond latency query responsiveness, random access to indexed subsets of data, and support for expressive ad-hoc queries and aggregations. Integrating a highly scalable and flexible operational database layer is essential to address these challenges and help companies act on the insights and intelligence created by their data lake.