A data lake is a centralized repository that stores large volumes of raw data in its native format, including structured, semi-structured, and unstructured data. It is designed for flexibility and scalability, allowing users to store data without defining a schema in advance. This makes it useful for storing diverse data types such as log files, images, documents, and sensor data, which may be used later for analytics, machine learning, or exploration.
Unlike a data lakehouse, a data lake does not include built-in support for features such as ACID transactions, schema enforcement, or strong data governance. As a result, organizations may need to add external tools or processes to ensure data quality and usability when working with a data lake directly. Data lakes offer low-cost, high-volume storage, but may require additional infrastructure to support analytics use cases.