Components of Data Warehouse
A typical data warehouse consists of four major components: a central database, ETL (extract, transform, and load) tools, metadata, and access tools. All of these components are designed to work rapidly, allowing you to acquire findings and analyze data on the fly.
The following are the four major components of a Data Warehouse:
The foundation of your data warehouse is a database. Traditionally, these have been traditional relational databases that run on-premises or in the cloud. However, in memory databases are rapidly gaining popularity as a result of Big Data, the need for true, real-time performance, and a drastic reduction in the cost of RAM.
Using a variety of data integration approaches such as ETL (extract, transform, load) and ELT, as well as real-time data replication, bulk-load processing, data transformation, and data quality and enrichment services, data is pulled from source systems and modified to align the information for rapid analytical consumption.
Metadata is information about your information. It identifies the data sets in your data warehouse’s source, usage, values, and other characteristics. Technical metadata specifies how to access data, including where it lives and how it is formatted, while business metadata gives meaning to your data.
Data warehouse access tools
Users can interact with the data in your data warehouse using access tools. Query and reporting tools, application development tools, data mining tools, and OLAP tools are all examples of access tools.
Need for Data Warehousing
A well designed data warehouse is the foundation for any successful BI or analytics programs.Its main job is to power the reports,dashboards and analytical tools that have become indispensable to business today.A data warehouse provides the information for your data-driven decisions and helps you make the right call on everything fro new product development to inventory levels.There are many benefits of a data warehouse.They are:
- Better business analytics
- Faster queries
- Improved data quality
- Historical insight
Also Read: Introduction to Multidimensional Data Model
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