Data Warehouse

A Data Warehouse (DWH), also known as an Enterprise Data Warehouse (EDW), represents the traditional approach to data collection, a practice established over 30 years ago. The DWH is crucial for integrating data from numerous sources, serving as a single source of truth, and managing data through processes such as cleaning, historical tracking, and data consolidation. It facilitates enhanced executive insight into corporate performance through management dashboards, reports, or ad-hoc analyses.

Data Warehouses are instrumental in analyzing various types of business data. Their importance is particularly evident when analytic demands clash with the performance of operational databases. Running complex queries on a database necessitates a temporary fixed state, which can disrupt transactional databases. In such scenarios, a data warehouse is utilized to perform the analytics, allowing the transactional database to continue handling transactions efficiently.

Another key characteristic of DWHs is their capability to analyze data from diverse origins (for example, combining Google Analytics with CRM data). This is possible due to the data being heavily transformed and structured through the ETL (Extract, Transform, Load) process.

There are two definitions of a data warehouse.

A data warehouse is a subject-oriented, integrated, time-variant and non-volatile collection of data in support of management’s decision making process. Bill Inmon, 1990 (sources: NUS Computing, and later in, 1992 the book of Building the Data Warehouse) ^e6fbe3

A data warehouse is a copy of transaction data specifically structured for query and analysis. Ralph Kimball, 1996 (The Data Warehouse Toolkit (Ralph Kimball) 1996, now in 3rd edition 2013)

Find more on Inmon vs. Kimball- Data Warehousing Approaches (History of Data Warehousing).

What is a Data Warehouse

Why having a Data Warehouse

• Great write up by GitLab: Enterprise Data Warehouse | The GitLab Handbook (Code in GitLab Data repo)
• Cloud Data Warehouses (SQL Data Warehouse)

• Data Engineering, the future of Data Warehousing?
• Inmon vs. Kimball- Data Warehousing Approaches
• Architectures:
  • Classical Architecture of Data Warehouse
  • Data Lake
  • Lakehouse
• Data Warehouse Automation (DWA)
• Concepts:
  • Data Engineering Concepts
  • Data Modeling
  • Data Engineering Vault

Origin: Data Warehouse vs Data Lake | ETL vs ELT | ssp.sh
References: Data Engineering ETL Business Intelligence Business Intelligence Engineer vs Data Engineer Why having a Data Warehouse What is a Data Warehouse Data Warehouse vs Data Lake Will a Data Lake replace the Data Warehouse, CDP (Customer Data Platform)