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In the modern data driven world, companies depend heavily on data warehouse to manage the large data volume. Wait! what exactly is ‘Data Warehouse’?
Data warehouse is a central repository for collecting, storing and analyzing information to provide better and meaningful insights, thereby supporting business intelligence activities.
As data storage paradigms shift with technological progress, the persistent demand for data warehouse is rising faster than ever. Unlike the conventional databases, data warehouse is architected to manage tons of data and complex queries, helping organizations to get updated and make timely decisions.
The term ‘Data Warehouse’ was first used in the year 1970 by Bill Inmon. Prism, the first product which is based the data warehouse was later launched in the year 1991.
Followed by the year 2000, My SQL, which is the first open source relational database management system was found.
Data warehouses are built to handle a large volume of data, while the accuracy of the data is heavily influenced by the effectiveness of ETL process.
There are different types of Data warehouses, each model serves different purposes.
Enterprise Data Warehouse is a centralized warehouse that integrates data from different departments enabling cross functioning analysis. Examples of EDW includes Teradata, and Oracle Exadata.
This type of Data warehouse is required for the operational needs of the organization to manage the day-to-day operations. This is mainly used for short time and daily activities such as employee activity records, attendance records etc. An example of ODS is IBM InfoSphere.
This type of Data warehouse is a subset, a smaller part of the Data warehouse that focuses on a specific unit or department within the business. For example, Marketing Data Warehouse can be used to analyze the marketing campaign performances and its impact.
Data warehouse serves as the core of an organization’s data strategy. Key data warehouse concepts must be followed while using the Data integration tools for an effective customer data integration. To boost the data warehouse efficiency, a robust ETL (Extract, Transfer, Load) process is vital. Discover how Trocco, a no-code ETL tool, can simplify the data integration and improve your data strategy.
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