A key book on data warehousing is W. H. Inmon's Building the Data Warehouse, a practical guide that was first published in 1990 and has been reprinted several times.

But if the shape of data warehousing projects can come in extremes, their basic management blueprint will not be too dissimilar. And they invariably stem from a few common--and common sense ...

Because “putting a data warehouse between two Tier 1 apps is a [bad] idea.” Companies tend not to treat their analytical systems “like Tier 1, business-critical components.” ...

The “data” part of the terms “data lake,” “data warehouse,” and “database” is easy enough to understand. Data are everywhere, and the bits need to be kept somewhere.

In 2015 it was Magic Quadrant for Data Warehouse and Data Management Solutions for Analytics. In 2017 we will enjoy the Magic Quadrant for Data Management Solutions for Analytics.

A data warehouse is a big IT project, and like many big IT projects, it can suck a lot of IT man hours and budgetary money to generate a tool that doesn't get used often enough to justify the ...

According to a recent Qlik report, a modern approach to data warehouse projects can address several key challenges, including improved cloud interoperability, replacing brittle legacy systems and ...

Data marts may be dependent on the data warehouse, independent of the data warehouse (i.e. drawn from an operational database or external source), or a hybrid of the two.

Image: phonlamaiphoto/Adobe Stock. Fivetran, the ETL and data pipeline company, has released its Cloud Data Warehouse Benchmark report. In partnership with Brooklyn Data Co., Fivetran studied five ...

The Boston College Enterprise Data Warehouse (EDW) supports reporting and analytics over a broad spectrum of University data. Developed in 2003, the EDW has advanced in terms of content, information ...