But there are many other ways to process data besides SQL, and there is a strong demand for different data storage patterns and query mechanisms that optimize the delivery of insight from that raw ...

Enterprise data warehouses, or EDWs, are unified databases for all historical data across an enterprise, optimized for analytics. These days, organizations implementing data warehouses often consider ...

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 ...

Oracle Corp. early today debuted a new version of its Autonomous Data Warehouse platform that promises to simplify enterprise analytics projects, as well as reduce costs. Companies use the ...

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

Essentially, a data warehouse is an analytic database, usually relational, that is created from two or more data sources, typically to store historical data, which may have a scale of petabytes.

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 ...

A quiet corner of Indianapolis may soon host an engine of the modern internet as the Project Flo Google data center inches ...

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.

Continuous Learning Many modern robot systems operate by using AI to process sensor data and make decisions about how to act across a wide variety of items and scenarios.

In response, Shuzhineng leverages an IoT platform as its core, integrating various sensing devices in the warehouse ...