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Data warehouse

A data warehouse is a centralized repository that stores large amounts of structured data from multiple sources—such as databases, applications, and data feeds. Its purpose is to support analytics and reporting by consolidating data, standardizing formats, and organizing it for efficient querying.

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  1. Data warehouse (en-US)

    A data warehouse is a centralized repository that stores large amounts of structured data from multiple sources—such as databases, applications, and data feeds. Its purpose is to support analytics and reporting by consolidating data, standardizing formats, and organizing it for efficient querying.

  2. How it works

    Data is typically extracted from source systems (ETL/ELT), transformed to match a consistent schema (for example, cleaning, deduplicating, and aligning data types), and then loaded into the warehouse. Many warehouses use a dimensional model (e.g., facts and dimensions) to make business questions easier to answer. They often include indexing, partitioning, and performance optimizations to speed up dashboards, ad hoc analysis, and historical trend reporting.

  3. Common uses

    Organizations use data warehouses for business intelligence (BI), KPI reporting, forecasting, and cross-department analytics. They also serve as a foundation for advanced analytics and machine learning workflows, where historical and integrated data is needed for training and evaluation.

FAQ

What’s the difference between a data warehouse and a database?

A database usually supports day-to-day transactions, while a data warehouse is optimized for analytical queries and historical reporting across many sources.

What does ETL/ELT mean?

ETL means Extract, Transform, Load; ELT means Extract, Load, Transform—transformations happen after loading in the target system.

Is a data warehouse only for large companies?

No. Smaller organizations can use cloud or managed solutions to centralize data for reporting and analytics, depending on needs and scale.

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