Data Cube vs. Data Warehouse for Business Intelligence
Blog post from Sigma
Agile business intelligence today benefits from the advancements in cloud technology, which have rendered the traditional debate between data warehouses and data cubes obsolete. Historically, data warehouses provided a centralized repository for cleaned and organized data, while data cubes offered faster query processing but required extensive modeling for new analyses. The emergence of cloud data warehouses, like Google BigQuery and Amazon Redshift, coupled with SaaS pipeline tools such as Fivetran and Matillion, has revolutionized data storage and processing by offering near-unlimited storage, affordable compute power, and simplified integration with popular data sources. These developments allow organizations to perform high-performance, OLAP-type workloads directly in the data warehouse without needing to extract data or build cubes, thus enabling more efficient and flexible data analysis. Modern cloud-native analysis tools further enhance this capability by allowing direct queries of live data while ensuring strict data governance, thereby facilitating decision-making based on real-time information.