A data warehouse is a database set up specifically for analysis rather than for running the business day to day. Copies of data from operational systems — sales, finance, HR, marketing — are loaded into it on a schedule, cleaned, standardised and kept as history. Products such as Snowflake, BigQuery, Redshift and Databricks are examples. Dashboards and reports then read from the warehouse instead of from a dozen separate systems.
Warehouses appear when a business outgrows point-to-point reporting. A retailer with 15 years of transactions, three former point-of-sale systems and a loyalty scheme wants one consistent customer history; loading it all into a warehouse, with one customer ID and one product hierarchy, makes that possible. The warehouse also protects operational systems from heavy analytical queries and keeps history that source systems may overwrite.
A warehouse is not a prerequisite for BI, and that is the most common confusion. Many businesses get their first years of value by connecting a BI tool directly to the systems they already run, and add a warehouse later when history, volume or consistency demands it. Rule of thumb: if you have one or two main systems, connect directly; if you have many, or need years of clean history, consider a warehouse — and choose a BI tool that works either way.
In Klayara, a warehouse is simply another data source: connect Snowflake, BigQuery, Redshift or Databricks live, or connect straight to your operational systems, and mix both on one dashboard. See the connector catalogue.