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Answer» A Data LAKE is a large-scale storage repository for structured, semi-structured, and unstructured data. It's a LOCATION where you can save any type of data in its original format, with no restrictions on account SIZE or file size. It provides a significant amount of data for improved analytical performance and native integration. A data lake is a huge container that looks a lot like a lake or a river. Similar to how a lake has various tributaries, a data lake has structured data, unstructured data, machine-to-machine communication, and logs flowing through in real-time. The following table enlists the differences between data lake and data warehouse: | Data Lake | Data Warehouse |
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| All data is stored in the data lake, regardless of its source or structure. The data is stored in its unprocessed state. When it is ready to be used, it is converted. | Data extracted from transactional systems or data consisting of quantitative measures and their properties will be stored in a data warehouse. The information has been cleansed and changed. | | Captures semi-structured and unstructured data in their original form from source systems. | Captures structured data and ORGANISES it according to defined standards for data warehouse purposes. | | The data lake is appropriate for those that perform in-depth analysis. Data scientists, for example, require advanced analytical techniques that include predictive modelling and statistical analysis. | Because it is highly structured, easy to use, and understand, the data warehouse is perfect for operational users. | | The cost of storing data in big data technology is less than that of storing data in a data warehouse. | Data warehouse storage is more expensive and time-consuming. | | The schema is usually developed after the data has been stored. This provides a great LEVEL of flexibility and convenience of data collecting, but it necessitates labour at the end of the process. | Schema is usually defined before data is saved. Work is required at the start of the process, but performance, security, and integration are all advantages. | | Users can access data in data lakes before it has been transformed, cleansed, or structured. In comparison to a traditional data warehouse, it allows consumers to get to their results faster. | Pre-defined inquiries for pre-defined data kinds are answered by data warehouses. As a result, any updates to the data warehouse take longer. |
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