What is Data Warehouse?
A Data Warehouse, sometimes categorized as an Enterprise Data Warehouse, (DW or DWH) is a data analysis and reporting system. Data Warehouses are fundamental storehouses of integrated data from single, or multiple sources, storing historical or current data in one location where data is utilized, creating reports for designated Enterprise users.
A DW is considered an integral component of business intelligence and describes a system used to analyze an organization's raw data. The Data Warehouse oversees the performance of a business database and monitors its agility when loading and retrieving data.
IT professionals on IT Central Station have certain core requirements when looking at the integration of a Data Warehouse. Some of these include which analytical capabilities are supported. For example, support of SAP HANA is viewed as a plus because it has necessary, analytical layers built within the Data Warehouse.
Other important criteria include the variety of supported data, structured and unstructured, and how they co-exist among various big data solutions. There are options where a Data Warehouse would have the capability to integrate or support other data management solutions. The inclusion of ETL tools, with extensibility, is always a preference in the Data Warehousing solution.
Some IT and DevOps professionals see Data Warehousing address both business and technical requirements because of the evolution from high-powered databases, with storage locally or in the cloud, (enhanced storage) to significant Enterprise information management solutions. Certain warehouse data is transferred from dedicated systems, such as Sales or Marketing. Data may be cleansed to ensure the quality of data before using it for reporting purposes.
Hence, ease of using in finding and retrieving data is essential. For Data Warehousing, IT and DevOps are focused on the total cost of ownership, scalability and performance features such as in-memory architecture, and row-based optics versus columnar or parallel processing. Storage optimization is key for data compression capability and caching. Of course, security and compliance for regulated organizations need support for data access control, masking, and auditing.