Demand for real-time and operational data is growing as consumers and enterprises expect applications to react quickly to changes, providing intelligent notifications and alerts. Cube aims to address this by creating a seamless experience for data engineers to work with streaming data alongside batched data, allowing for a single, headless data access layer regardless of the data type. Historically, working with real-time data has been challenging, but recent innovations in streaming SQL technologies have made it easier to process and analyze streaming data using SQL. Cube now supports streaming SQL engines and exposes streaming data via its REST, GraphQL, and SQL APIs, enabling developers to build real-time data apps without needing a new language or specific integrations. Additionally, Cube is introducing lambda pre-aggregations to address the complex problem of merging batch and streaming data, providing a single interface for unioned data and simplifying data consumption. The company plans to expand its support for Materialize, Flink SQL, and Spark Streaming in the near future, making it easier for users to build powerful real-time applications.