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Traditional data warehouses are effective for data exploration and analysis but fall short in enabling real-time operational reactions, a gap that has become increasingly evident as companies seek to leverage data for immediate decision-making and competitive advantage. The rise of the cloud and software integration has accelerated digital transformation and the importance of data as a critical resource, yet even advanced companies face delays in accessing real-time insights, often relying on pre-aggregated data views that do not reflect the current state of their business. The shift towards operational analytics emphasizes not just extracting insights from data but also reacting to those insights instantaneously, a necessity highlighted by use cases like e-commerce logistics and car-sharing applications. However, achieving real-time operational analytics has been challenging due to the complexity of building data pipelines, infrastructure maintenance, and the limitations of existing data warehousing tools, which are not optimized for real-time processing or developer accessibility. Tinybird is presented as a solution that enables developers to achieve operational analytics at scale through secure and dynamic APIs, simplifying the process and allowing data engineers and developers to transform real-time data into business value with minimal effort.