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 ...

In data warehousing, the logical model is often an afterthought or merely a carbon copy of the physical sans platform-specific properties. That limits the visibility and the use of the model to a ...

Current enterprise data architectures include NoSQL databases co-existing with RDBMS. In this article, author discusses a solution for managing NoSQL & relational data using unified data modeling.

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 ...

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

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

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

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.