Data lakes vs. data warehouses: A deep dive into understanding the key differences

Data lakes and data warehouses are two popular options for managing big data, but they have distinct differences. A data lake stores raw, undefined, and unprocessed data in its original format, while a data warehouse stores structured and filtered data that has already been processed for a specific purpose. Key differences between the two include their data models, sources, storage, processing, cost, agility, data quality, governance, time-to-value, scalability, use cases, and processing tools. DoubleCloud is a cloud management platform that provides solutions to automate and analyze infrastructure on a cloud scale, helping businesses consolidate data from multiple sources into a single data warehouse or data lake. When choosing between a data warehouse and a data lake, it's essential to consider factors such as the type of data needed, how often data is updated, the analytics planned, and budget constraints.