The Lambda and Kappa architectures are two velocity-based data architectures that have gained popularity in recent years due to their ability to handle large volumes of data and provide low-latency query results. The Lambda architecture, developed by Nathan Marz in 2011, is a scalable, fault-tolerant, and flexible system for processing large amounts of data using batch-processing and stream-processing methods. It consists of two separate processing systems: a batch processing system that stores the results in a centralized data store, and a stream processing system that processes data in real-time as it arrives. The Kappa architecture, on the other hand, is a single technology stack for both real-time and batch data processing, relying on streaming architecture to provide scalability, fault tolerance, and flexibility. While both architectures have their advantages, they also have drawbacks, such as the cost of infrastructure and the need for mechanisms to overcome late-arriving data issues.