A data warehouse is a system commonly used to manage, store and analyse data at scale. In a data warehouse, there is a centralised repository of data, which enables you to manage large volumes of data and get access to historical data from various sources.
It is designed for efficient data analysis, reporting, and business intelligence, enabling organisations to make informed decisions based on past and current data. That said, a data warehouse is not just a database — it is the entire system.
There are multiple components to operating a data warehouse:
- Defining data required and bringing the data into a datawarehouse. (Often, teams write jobs using tools like Airflow to get data in).
- Storing and transforming the data: The way this happens, depends deeply on the budgets and business requirements. Some data could be stored in raw format (also called bronze data) to save costs, while some could be processed slightly (silver data) or enriched significantly (gold data) to make analytics / reporting and other tasks very easy.
Modern day urgency and dependency on data has created multiple new concepts like DataLakes and Data Lakehouses.
Data warehouse vs Data lake vs Lakehouse: https://medium.com/big-data-processing/data-warehouse-vs-data-lake-vs-data-lakehouse-bde5676282e4
What is a Data warehouse by Microsoft Azure: https://azure.microsoft.com/en-in/resources/cloud-computing-dictionary/what-is-a-data-warehouse
