Data lake architecture is used to describe the design structure in which a data lake operates by collecting, storing, processing, managing, and accessing data. Some layers of data lake architecture can include data ingestion, data storage, data processing, governance, and analytics. Data can flow into the lake from databases, applications, websites, devices, or third-party sources. Data will then be stored in a scalable system, from where it can be processed when necessary. Metadata and governance systems can allow organizations to know the origin of data, users with permissions to access the data, and how the data is to be managed. The right data lake architecture will depend on the needs of the organization, data sources, security considerations, and analytical needs.