Data warehouses also feature metadata, which contributes to their highly organized structure and quality assurance. Metadata governs the entire warehouse structure and includes naming conventions, information about data sources and refresh schedules.
Because multiple databases feed data warehouses, data in each database is organized into tables with columns that use such descriptors as strings, integers and data fields. These tables are built using schemas that are typically defined before building out the data warehouse, and usually include some sort of aggregation forming logically sound groups of data sources based on business unit or initiative. Structuring your data in this way makes it very simple to expose data to the analytics and visualization layers of the data warehouse.
Why Should I Use a Data Warehouse?
While traditional databases are great for rapid, accurate data retrieval in the moment, a data warehouse relies on the power and capacity of multiple databases to give organizations a long-range view of information over time so they can analyze trends and performance. They are ideal for data aggregation.
Because data warehouses easily support complex queries, they are ideally suited for research, reflection and analysis into business operations and intelligence across multiple databases. This speeds up the analysis process and surfaces business insights to help your teams make crucial decisions.
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