Sometimes the resulting architecture has a hub, sometimes not. That’s different from data integration’s architectural focus on interfaces and data transformations, which almost always hinge on a hub.

A hub-and-spoke data warehouse architecture pattern works by following a series of steps. First, the data from various sources is extracted, transformed, and loaded (ETL) into the hub.

With zenon 15, COPA-DATA expands its support for HTML5-based web visualization, enabling fast and intuitive screens that work ...

Data integration tools are widely available on the market, each with its own advantages and disadvantages. ETL (extract, transform, load) tools, for instance, allow data architects to extract ...

In a previous article, I discussed redefining the challenge facing companies that want to become data-driven. The way most people think about this problem—and the most commonly proposed solution ...

Factories are increasingly adopting a Unified Namespace architecture—an event‑driven, centralised framework that unites data ...

Their serverless architecture ensures automatic scaling and eliminates the need for complex infrastructure ... DIaaS platforms provide a centralised hub for managing data integration workflows, ...

Explore Ads Data Hub – Google's tool for privacy-centric advertising insights, data integration, campaign optimization and more. ... Ads Data Hub: Setup and architecture.

The Next-Generation Product Catalog and Data Integration Hub is a backend service designed to manage complex product catalogs and facilitate seamless data integration across multiple platforms. It ...

As digital transformation becomes essential, cloud-based data integration is revolutionizing how businesses manage complex data environments. Srujan Reddy Anugu, an expert in cloud computing, examines ...