Description: The layered data architecture is a design pattern that separates data management into different layers for better organization and scalability. This approach allows organizations to handle large volumes of data more efficiently, facilitating the integration, storage, and analysis of information. In a layered data architecture, each layer has a specific function, helping to simplify the complexity of the overall system. Typical layers include the ingestion layer, where data is collected; the storage layer, which can be a data lake or a data warehouse; and the processing layer, where transformations and analyses are performed. This modular design not only improves system maintainability but also allows organizations to scale their data operations according to their needs. Additionally, by separating functions, it facilitates the implementation of new technologies and tools in each layer without affecting the rest of the system. In the context of Big Data and data architecture, this structure is especially relevant as it allows for efficient handling of structured and unstructured data, optimizing the flow of information and ensuring that data is accessible and usable for decision-making.
