A traditional data lake uses batch processing to store and analyze structured, semi-structured, and unstructured data from various sources in a centralized repository. However, real-time or live data streaming occurs while data is in motion through a system, enabling immediate analysis and reporting of ongoing events. Real-time data lakes store real-time data as soon as it is generated without making assumptions about the data's structure or type, providing flexibility to adapt to current business scenarios and conditions. They are ideal for teams that need to analyze data in real-time across multiple sources, offering benefits such as cost-effectiveness, scalability, simplicity, and data integrity. Real-time data lakes also enable continuous processing of high-velocity data influx by distributing the load across multiple nodes and enabling parallel processing.