What is Data warehouse?
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence.[1] Data warehouses are central repositories of data integrated from disparate sources. They store current and historical data organized in a way that is optimized for data analysis, generation of reports, and developing insights across the integrated data.[2] They are intended to be used by analysts and managers to help make organizational decisions.[3] Data Warehouse and Data-Marts overviewData Warehouse and Data mart overview, with Data Marts shown in the top right. The basic architecture of a data warehouse The data stored in the warehouse is uploaded from operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting. The two main workflows for building a data warehouse system are extract, transform, load (ETL) and extract, load, transform (ELT). Component The environment for data warehouses and marts includes the following: Source systems of data (often, the company's operational databases, such as relational databases[3]); Data integration technology and processes to extract data from source systems, transform them, and load them into a data mart or warehouse;[3] Architectures to store data in the warehouse or marts; Tools and applications for varied users; Metadata, data quality, and governance processes. Metadata includes data sources (database, table, and column names), refresh schedules and data usage measures. Architecture The different methods used to construct/organize a data warehouse specified by an organization are numerous. The hardware utilized, software created and data resources specifically required for the correct functionality of a data warehouse are the main components of the data warehouse architecture. All data warehouses have multiple phases in which the requirements of the organization are modified and fine-tuned. Data warehouse in org These terms refer to the level of sophistication of a data warehouse: Offline operational data warehouse Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented database. Offline data warehouse Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting. On-time data warehouse Online Integrated Data Warehousing represent the real-time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data Integrated data warehouse These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.
In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence.[1] Data warehouses are central repositories of data integrated from disparate sources. They store current and historical data organized in a way that is optimized for data analysis, generation of reports, and developing insights across the integrated data.[2] They are intended to be used by analysts and managers to help make organizational decisions.[3]
Data Warehouse and Data-Marts overviewData Warehouse and Data mart overview, with Data Marts shown in the top right.
The basic architecture of a data warehouse
The data stored in the warehouse is uploaded from operational systems (such as marketing or sales). The data may pass through an operational data store and may require data cleansing for additional operations to ensure data quality before it is used in the data warehouse for reporting.
The two main workflows for building a data warehouse system are extract, transform, load (ETL) and extract, load, transform (ELT).
Component
The environment for data warehouses and marts includes the following:
Source systems of data (often, the company's operational databases, such as relational databases[3]);
Data integration technology and processes to extract data from source systems, transform them, and load them into a data mart or warehouse;[3]
Architectures to store data in the warehouse or marts;
Tools and applications for varied users;
Metadata, data quality, and governance processes. Metadata includes data sources (database, table, and column names), refresh schedules and data usage measures.
Architecture
The different methods used to construct/organize a data warehouse specified by an organization are numerous. The hardware utilized, software created and data resources specifically required for the correct functionality of a data warehouse are the main components of the data warehouse architecture. All data warehouses have multiple phases in which the requirements of the organization are modified and fine-tuned.
Data warehouse in org
These terms refer to the level of sophistication of a data warehouse:
Offline operational data warehouse
Data warehouses in this stage of evolution are updated on a regular time cycle (usually daily, weekly or monthly) from the operational systems and the data is stored in an integrated reporting-oriented database.
Offline data warehouse
Data warehouses at this stage are updated from data in the operational systems on a regular basis and the data warehouse data are stored in a data structure designed to facilitate reporting.
On-time data warehouse
Online Integrated Data Warehousing represent the real-time Data warehouses stage data in the warehouse is updated for every transaction performed on the source data
Integrated data warehouse
These data warehouses assemble data from different areas of business, so users can look up the information they need across other systems.
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