A brief history of data warehousing and first-generation data warehouses

TLDR: This chapter provides an overview of the history of data warehousing with a focus on the first-generation data warehouse, which evolved to include disciplined data ETL from legacy applications in a granular, historical, integrated data warehouse.

Abstract: This chapter provides an overview of the history of data warehousing. Data warehousing has come a long way since the frustrating days when user data was limited to operational application data that was accessible only through an IT department intermediary. Data warehousing has evolved to meet the needs of end users who require integrated, historical, granular, flexible, and accurate information. The first-generation data warehouse evolved to include disciplined data ETL (extract/transform/load) from legacy applications in a granular, historical, integrated data warehouse. With the growing popularity of data warehousing came numerous changes—volumes of data, a spiral development approach, heuristic processing, and more. As the evolution of data warehousing continued, some mutant forms emerged like active data warehousing, federated data warehousing, star schema data warehouses, and data mart data warehouses. While each of these mutant forms of data warehousing has some advantages, they also have introduced a host of new and significant disadvantages. Therefore, the time for the next generation of data warehousing has come.