In similar fashion, data hubs minimize the spaghetti of point-to-point linkages between applications and provide key business process integration points in the enterprise.

As data is the new oil, we need a new storage architecture to leverage disparate data lakes. Pure Storage introduced the concept of the "data hub" and analyst Patrick Moorhead dissects this.

Data lakes were built for big data and batch processing, but AI and machine learning models need more flow and third party connections. Enter the data hub concept that'll likely pick up steam.

Veritas NetBackup and Pure Storage Data Hub Architecture Enable Data Protection for AI Clusters, Big Data and IoT SINGAPORE, Oct. 10, 2018 /PRNewswire/ -- Veritas Technologies, the worldwide ...

With Snowflake AI Data Cloud, IndiGo aims to unify enterprise data, enable faster decision-making, and accelerate its ...

Cloudera and Informatica have partnered to create a new Data Warehouse Optimization (DWO) reference architecture specifically for Enterprise Data Hub deployments with the goal of helping reduce data ...

Pure's data solutions enable SaaS companies, cloud service providers, and enterprise and public sector customers to deliver real-time, secure data to power their mission-critical production ...

Evelson recommends creating an enterprise data hub based on Hadoop or another similar, low-cost platform and then creating BI apps as “spokes” off of that hub.

Using the hub-and-spoke model of a centralized data hub, businesses can easily achieve their real-time data goals, says Hanadi Salameh, an Enterprise Digital Architect at Cognizant.

An Enterprise Data Hub is a modern data architecture which is enables enterprises to compliment their existing data warehouse architecture. Diyotta readily provides capabilities to exploit the ...