With the cloud democratizing data and pressure to modernize, enterprises don’t need to choose between the strengths of cloud data warehouses and cloud data lakes: co-existence models allow them to ...

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 warehouse architectural design Types of data models and dimensions Best practices for implementing and managing enterprise data warehouse in 2022 What is a data warehouse?

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 ...

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 ...

Inmon versus Kimball is one of the biggest data modelling debates among data warehouse architects. Here is some help to select your own approach.

Data warehouse designs are the foundation of business intelligence projects. Find out 5 mistakes you need to avoid now.

How to get more done with less using a data warehouse Data in the warehouse is subjected, structured, and sectionalized so the consumption of the data for AI and analytics is easy.

One of the most important shifts in data warehousing in recent times has been the emergence of the cloud data warehouse. Previously, setting up a data warehouse required a huge investment in IT ...

In this model, a service provider is responsible for setting up, maintaining, securing and upgrading a data warehouse – complete with the handling of all associated software and hardware stacks.