Data analytics have either been centralized or decentralized. Data mesh tried to fix that. The hub-and-spoke model goes further.

As companies implement identity resolution solutions, many are left with the challenge of needing to merge offline customer ...

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 ...

Data lakes and data warehouses are achieving a measure of success in modern data architectures, but the emergence of the data lakehouse offers new challenges and opportunities for database ...

The Top 25 Firm plans to launch a data warehouse service for smaller organizations this year and has been using AI to develop some of its rollout strategies.

In the spirit of capturing and describing this data-warehouse revolution and the drivers shaping Data Warehouse 2.0, Oracle has assembled a list of the Top 10 Data Warehousing Trends for 2013, and ...

Oracle Data Warehouse and Amazon Redshift are two popular data warehousing solutions, but which one has your organization's ideal features and capabilities? Read this comparison to find out.

Part 4 of CRN’s Big Data 100 takes a look at the vendors solution providers should know in the data warehouse and data lake systems space.

Although difficult, flawless data warehouse design is a must for a successful BI system. Avoid these six mistakes to make your data warehouse perfect.

Data modeling is the framework that lets data analysis use data for decision-making. A combined approach is needed to maximize data insights.