A data warehouse is considered an environment because it encompasses a complex ecosystem of tools, processes, and technologies designed to store, manage, and analyze large volumes of data. It integrates various data sources, supports ETL (Extract, Transform, Load) processes, and facilitates data governance, making it a comprehensive framework rather than a singular solution. Additionally, a data warehouse requires ongoing maintenance, optimization, and user training, highlighting its role as a dynamic environment that adapts to evolving business needs.
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.
A data warehouse typically uses a relational database optimized for analytical queries, such as Oracle, SQL Server, or Snowflake.
A data model is a (relatively) simple abstraction of a complex real-world data environment. Database designers use data models to communicate with applications programmers and end users. The basic data-modeling components are entities, attributes, relationships, and constraints. Business rules are used to identify and define the basic modeling components within a specific real-world environment.
DATABASES - DBMS, DATA WAREHOUSE - DW, BUSINESS INTELLIGENCE - BI, Enterprise Resource Planning - ERP, Customer Relationship Management - CRM, Supply Chain Management - SCMare few of the Applications that IT People use to manage DATA to support Business requirements.
The tools and processes that surround us to gather and interpret data
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.
In computing, a warehouse can be likened to a data warehouse, which is a centralized repository for storing large volumes of structured and unstructured data. While it serves as a storage solution, it is not necessarily temporary; data warehouses are designed for long-term storage and analysis rather than short-term, transient storage like memory (RAM). Unlike volatile memory, which loses data when the power is off, a data warehouse retains data for ongoing use and reporting.
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.
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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.
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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.