it's data warehouse....data warehouse: it is a collection of multiple databases or it it is repository of data.data mining it is the process of extracting data from data warehouse.
Data warehouse is a house where current as well as historical data can be stored.
Data warehouse is the database on which we apply data mining.
Data marts are combined into a data warehouse cannot be built alone without considering data marts. Both has equal importance to built proper data warehouse.
Metadata is data about data that provides information such as the structure, format, and characteristics of the data stored in a data warehouse. It is used in data warehouse architecture to facilitate data integration, data governance, and data lineage. Metadata helps users understand and manage the data in the data warehouse efficiently.
This is an informal term referring to data retrieval and manipulation in a Data Warehousing Systems. We can picture a data warehouse as a cube of data, where each axis of the cube represents a dimension. To "slice" the data is to retrieve a piece (a slice) of the cube by specifying measures and values for some or all of the dimensions. When we retrieve a data slice, we may also move and reorder its columns and rows as if we had diced the slice into many small pieces. A system with good slicing and dicing makes it easy to navigate through large amounts of data.
Every data structure in the data warehouse contains the time element. Why?
One of the biggest benefits is that you can archive your data to a data warehouse. This can keep your main "production" database smaller which can provide some performance benefits. Also you can use the data warehouse to run complex queries and data-mining without adverse effects on the performance of your "production" application.
A data warehouse architecture is similar to various relational database systems. What makes the best architecture is the organization of the warehouse itself and the data it consist of.
Data warehouse is the pool of huge amount of data. The data in data ware house can be archived. And when the data is needed you can extract it from the archived files.
A distributed data warehouse is a type of data warehouse architecture where data is distributed across multiple servers or nodes in a network. This allows for improved scalability, performance, and fault tolerance compared to a centralized data warehouse. Distributed data warehouses can handle large volumes of data more efficiently by spreading the workload across multiple nodes.
A data warehouse stores structured data from various sources for analysis and reporting. It typically includes historical data, organized into tables, aimed at supporting decision-making processes. Data warehouses are optimized for complex queries and data aggregation.