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Data warehouse

A data warehouse is a centralized location for storing data, typically from many different sources.

Companies often have data in many places around their business—in their CRM, in logs of events, in marketing automation tools, and inventory management systems, in financial systems of record, and in many other places. To understand and report on this data (e.g., to track the number of new orders or see how many people are taking a particular action in a product), data teams often want to centralize their data into a single place. This not only provides a single point of access, but it also allows analysts to easily combine data from different sources in a single analysis.

An example: How do data warehouses help companies sort data?

Suppose we want to understand how many paying customers use our product every day. Product activity data is recorded in event logs, information that connects users to their companies is stored in our application database, and customer contract data is in Salesforce. In order to answer this question, we have to combine data from all three sources.

Without a data warehouse, this process would be difficult, if not impossible. We could export all of this data from its original locations, and combine it using Excel or a scripting language like Python. That’s often not possible though: These datasets are often too large for tools like Excel to work with directly. Moreover, extracting data in this way would be a time-consuming and error-prone process—not what you want for answering urgent questions or reporting on metrics that you want to update frequently.

Data warehouses are designed to solve this problem. They operate as a central clearing house for an organization’s data. They can store and process very large amounts of data, and often support robust analytical operations designed to help analysts manipulate and aggregate data.

A data warehouse is a centralized location for storing data, typically from many different sources.

Choose the right cloud-based data warehouse for your team using this guide.

Related terms:

Analytics architecture, ETL tools
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