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 ...

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 lakes are cool, but you don’t have to jump in head-first. It’s easy to start by dipping a toe: Integrating a legacy data warehouse into a data lake leverages the structured systems that ...

As companies embrace digital trans­formation across the enterprise, it is data and the effective use of it that determines whether new technologies such as AI, auto­mation, and analytics will be ...

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 ...

If data pipelines and streams are the future, why are we still thinking of data as static?

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.

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 ...

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

Paige Roberts, Vertica open source relations manager, provided a contemporary definition of the data warehouse and its "deploy anywhere" orientation during her presentation at Data Summit Connect 2021 ...

Fivetran releases cloud data warehouse benchmark to compare top vendors Your email has been sent Fivetran, the ETL and data pipeline vendor, has released a benchmark report to compare top data ...