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What is Data Warehouse?

Many organizations tend to have a bulk of raw data that resides in different operational systems or data sources. The absence of a unified database leads to a time-consuming data-gathering process.

Data warehouse is a real godsend to many companies because it is a practical tool for storing and optimizing data, in which a lot of corporations opt to generate more business benefits. Thus, to succeed as a BI analyst or data science, one must grasp the concept of data warehouse (DW).  

What is Data Warehouse?

What is Data Warehouse exactly? A data warehouse (DW) is a centralized site that brings all the raw data together and optimizes it for further analysis and visualization. Data warehousing is a structured application for BI, rendering data-harnessing much more time-effective.

The Extract, Transform and Load (ETL) processes mean that data specialists are required to extra data from multiple data sources, transform it to store it in a suitable format for querying and analysis purpose. Being an integral part to produce performance metrics and predictive insight, the ETL process is a recurring process that is maintained and updated on a daily basis so as to instant and strategic business decisions.

Further, knowledge about SQL software is a big plus for employers. Through SQL, IT specialists can help design and implement data warehouses, and integrate and visualize data using analytics. Experience in SQL can certainly help IT specialists to climb their career ladders and stand out among other BI analysts.

Data-Warehousing Application across Industry

The reason why IT talents with Data Warehousing knowledge are highly sought after is that various industries, including retail, finance and banking, health care, are seeking related expertise. For instance, Data Warehousing has great importance to the retail sector because of its ability to generate solutions such as better in-store placement and product pricing.

With a centralized database, valuable information such as which items are being purchased the most, which row in the store do they belong to and overall product sales can be easily rendered. It allows the merchandisers to quickly predict the products they need to replenish.