A data warehouse is defined as a central repository that allows enterprises to store and consolidate business data extracted from multiple source systems for the task of historical and trend ...

First, there was a data warehouse – an information storage architecture that allowed structured data to be archived for specific business intelligence purposes and reporting. The concept of the ...

To build a data warehouse, data must first be extracted and transformed from an organization’s various sources. Then, the data must be loaded into the database in a structured format. Finally, an ETL ...

Data quality is paramount in data warehouses, but data quality practices are often overlooked during the development process.

This is because a Warehouse Native CDP is built on top of data lakes or warehouses supported by Amazon Web Services Inc., such as S3 and Redshift, Omwega explained.

Has the traditional data warehouse finally reached the end of its life? If so, what will follow it? Will it be a hybrid? We find out.

Lakehouse architectures are gaining steam as a preferred method for doing big data analytics in the cloud, thanks to the way they blend traditional data warehousing concepts with today’s cloud tech.

A data lakehouse addresses these typical limitations of a data lake, as well as data warehouse architecture, by combining the best elements of data warehouses and data lakes to deliver significant ...

It’s felt obvious for some time that, as an industry, we’ve been trying to shove square data warehousing tools into round, data-driven application holes. But it wasn’t until I read ...