To unlock the latent power of application data that exists across the data estate, developers need a powerful API that allows them to query data in various ways and perform CRUD operations, sophisticated aggregations, and data transformations. They also require a versatile query engine and indexing for efficient results, as well as tools to monitor query performance and optimize database queries. To make sense of voluminous streams of time series data, developers need optimized time series collection types, clustered indexes, and mechanisms to automate data lifecycle management. Creating intelligent applications that automatically react to events in real-time requires capabilities such as change streams, triggers, and functions. Combining live application data with aged data for predictions demands the ability to query, blend, and analyze data from various sources, including hot database storage and cooler cloud storage. Finally, bringing analytics into applications without compromising performance necessitates a distributed architecture, sophisticated replication techniques, and a unified platform that manages both transactional and analytical workloads.