Data Warehouse
we discuss here what is data warehouse in brief, In industry lots of data available from day to day transaction history. the whole data saved in operational database, the operational database include text data, video data, audio data, 2D, 3D , presentation, excel data etc. that all data saved in operational database but fallow unstructured format. in that data not saved clean format, all data saved with noisy and incorrect data.
In DW collect a useful data from operational database system. DW is important factor in big industry for saving data with good manner, In that include data with useful manner and that data is used for DSS(decision support system). the all data saved in DW is a variant data, non-volatile and subject oriented data. In DW saved all historical data from that industry.. the all historical data merged in to single DW.
In data warehouse consist informational data. the data extracted from operational database bu before extract that data was cleaned and summarized. in fallowing figure shows the main three methods that are used for getting data from DW.
Data extract from DW with –
1.Query Tool
2.OLAP (Online Analytical Processing) Tool
3.Data Mining Tool
Data Warehouse Features-
1.Subject Oriented-
Data warehouse is basically subject oriented means in DW saved a data with clean manner. the all data saved in Dw indicating a historical condition about that organization.ex- sales information, customer information
2.Integrated-
In DW data coming from various data sources like operational databases, relation database that all data integrated enhancing for analysis of data.
3.Time Variant-
In DW historical data saved with a particular time period. that all data used in future with data mining time series analysis method for predict a future outcomes.
4.Non-volatile-
Data warehouse extract data from different data sources but if new data added in DW the previous data can not be deleted from DW so it fallows non volatile property.
Explanation :
A data warehouse is a centralized system designed to store, manage, and analyze large volumes of data collected from multiple sources within an organization. It serves as a foundation for business intelligence (BI), reporting, and decision-making processes by providing a consolidated, historical view of organizational data. Unlike operational databases that handle daily transactions, a data warehouse focuses on analytical processing and long-term data storage.
The primary purpose of a data warehouse is to integrate data from diverse sources—such as transactional systems, customer relationship management (CRM) tools, and enterprise resource planning (ERP) systems—into a single, consistent format. This integration allows organizations to analyze data across different departments, identify patterns, and make strategic decisions. The process of preparing data for the warehouse involves three key steps: Extract, Transform, and Load (ETL). Data is first extracted from multiple sources, transformed to ensure consistency and accuracy, and then loaded into the warehouse for analysis.
A data warehouse is typically structured using a schema such as the star schema or snowflake schema, which organizes data into fact tables (quantitative information like sales or revenue) and dimension tables (descriptive information like time, region, or product). This design supports efficient querying and reporting.
One of the main features of a data warehouse is its ability to store historical data, enabling trend analysis, forecasting, and performance tracking over time. It supports Online Analytical Processing (OLAP), which allows users to perform complex queries, drill down into details, and view data from multiple perspectives.
The benefits of using a data warehouse include improved decision-making, data consistency, faster query performance, and enhanced data quality. Organizations use data warehouses to generate insights that support business strategy, marketing analysis, financial planning, and customer behavior analysis.
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