A data warehouse is a repository that holds data relevant to analysis — meaning, it has been cleaned to fit a specific relational database schema, it’s organized into tables, and it’s defined by data types, relationships, and rules. The data stored in a data warehouse is processed and already in use, which means it already has a specific purpose. Data and business analysts use data warehouses to answer specific business questions and/or create visualizations, dashboards, and reports.
The data warehouse created access to organized data within enterprise organizations by centralizing data within a single platform in an archived, structured way. Data warehouses process and transform data for analytics in a structured database environment where data can be queried to help make business decisions. They can often be seamlessly integrated with visualization tools like Tableau and Power BI to derive insights.
The data warehouse allows for historical insights, enabling businesses to look back at data and to react, but the data warehouse does not allow for predictive activity due to its performance restraints. Most data warehouses were designed taking into consideration the requirements data scientists have when performing business intelligence initiatives, not advanced analytics, and that’s why many organizations struggle to implement machine learning and artificial intelligence solutions with data warehouses today.
Data lakes and data warehouses complement each other in a data workflow. Ingested company data is stored immediately into a data lake. If a specific business question comes up, a portion of the data deemed relevant is extracted from the lake, cleaned, and exported into a data warehouse for analytics use-cases and business decisions.NEXT