A data lake is a centralized repository where structured, semi-structured, and unstructured data types are stored at any scale for processing, curation, and analytics. Data lakes support batch and real-time streams to combine raw data from diverse sources into the repository without a predefined schema. The need for scalable solutions that can manage large datasets has led organizations to look towards data lakes as a pivotal data management solution for machine learning teams. A data lake architecture typically comprises several layers dedicated to specific functions, including data sources, data ingestion, data persistence and storage, data processing layer, analytical sandboxes, data lake zones, and data consumption. Best practices for setting up a data lake include defining clear objectives, robust data governance, scalability, prioritizing security, encouraging a data-driven culture, and quality control. On-premises data lakes offer control and security, while cloud-based data lakes provide scalability and cost efficiency. Data lakes are evolving with advanced analytics and computer vision use cases, emphasizing the need for adaptable systems and adopting forward-thinking strategies.