Databases have traditionally been designed for one of two primary functions: analytical databases help businesses identify trends and answer complex questions, while operational databases manage daily transaction records, such as orders and payments. However, CedarDB, a Munich-based database engine, is breaking this mold by supporting both functions, a shift that has gained significance with the rise of agentic AI, according to cofounder and CEO Moritz Sichert.
Background and Development
CedarDB has been in development for the past decade, but its current momentum is driven by enterprise companies that find their existing data stacks inadequate for quickly answering analytical questions posed in natural language. The company challenges the traditional divide between analytical and operational queries, focusing on efficiency.
CedarDB is a spinout from the Technical University of Munich, based on the Umbra research project, which aimed to outperform legacy database engines in processing speed by leveraging modern hardware. With five Umbra researchers as cofounders, CedarDB promises faster performance without the need for additional CPUs or processing power, requiring minimal changes from customers.
Key Features and Compatibility
A significant aspect of CedarDB is its compatibility with PostgreSQL, a popular open-source database system. This compatibility helps keep switching costs low, as users do not need to rebuild their applications. According to Sichert, this decision aligns with the logic that large language models (LLMs) are most familiar with due to their training.
