What is a characteristic of Data Lakes compared to traditional data warehouses?

Data lakes are designed to handle vast amounts of varied data types, including unstructured, semi-structured, and structured data. This ability to store unstructured data formats is a key characteristic that distinguishes data lakes from traditional data warehouses, which primarily focus on structured data that is organized in a predefined manner.

In a data lake, the data is ingested in its raw form, allowing users to analyze and process it later, thus providing greater flexibility and scalability for handling diverse data sources such as social media streams, log files, and multimedia content. This capability enables organizations to derive insights from a broader array of information, accommodating the growing need for data analytics in various forms.

The other options reflect characteristics of traditional data warehouses, which emphasize a more rigid structure, predefined schemas, and often prioritize speed of access to structured data rather than flexibility in data types.