Data lake architecture means the components and design used in collecting, storing, processing, securing, managing, and analyzing data in the data lake. An architecture design might have several layers working together in handling information from various sources. For instance, the ingestion layer collects information from applications, databases, API's, websites, and other sources. Information is stored in the storage layer where it is kept as structured, semi-structured, and unstructured data in its raw form. The processing tools then prepare the collected information for analysis. The governance and security layers will enable the organizations to have control over the data access, management of data quality, protection of sensitive information, and monitoring the use of information. Analytics and visualization tools will be used in transforming the stored information into actionable insights. Knowing what a data lake is can be much easier by looking at its architecture because the architecture shows how the information moves from the source systems to storage and finally to analysis. The properly designed data lake architecture enables businesses to scale their data environment and do reporting, machine learning, and advanced analytics without rebuilding the whole system.