Why Real-Time and AI Break the Classic Data Stack

The text discusses the challenges faced by companies using a classic data stack for real-time, data-intensive products, highlighting the issues that arise when separate systems for transactional processing (OLTP), analytical processing (OLAP), and search are combined to meet increasing demands for real-time analytics and AI features. The "classic stack" often leads to complexity and scalability issues as new requirements necessitate additional systems, creating a complicated architecture that struggles to deliver real-time performance reliably. SingleStore is presented as an alternative, offering a unified HTAP system capable of handling operational queries, analytics, and modern search patterns in real-time on a single dataset, thus reducing the need for multiple systems and minimizing the associated operational overhead and costs. The text suggests that companies should consider transitioning to a unified real-time platform when real-time data becomes critical to their product, as this approach simplifies architecture, reduces costs, and enhances the scalability of the product by treating real-time as a default rather than an exception.