Learn about four data architecture patterns that can help you integrate and analyze data from different sources: data warehouse, data lake, data mesh, and data fabric.

The goal of data architecture is to translate business needs into data and system requirements, and to manage data and its flow through the enterprise. Many organizations today are looking to ...

The headless data architecture is the formalization of a data access layer at the center of your organization. Encompassing both streams and tables, it provides consistent data access for both ...

A headless data architecture means no longer having to coordinate multiple copies of data and being free to use whatever processing or query engine is most suitable for the job. Here’s how it works.

The data architect is responsible for visualizing and designing an organization’s enterprise data management framework, which describes the processes used to plan, specify, ...

Simultaneously, semantic knowledge graph technology is optimal for implementing data fabrics. This architecture entails integrating data from a plethora of sources, data types, schema and other ...

To gain business value from data, enterprises need to get their data architecture right – and the right business leadership and culture is critical to that.

Data architecture, however, spans the organization and takes a high-level, holistic view, whereas data modeling focuses on specific systems or business cases. In any case, the architecture or ...

AI fabric architecture is modular, scalable, and future-proof and the key to organizational excellence in the modern business landscape Time flies in the world of data analytics and artificial ...

Data Architecture of Source-Grid-Load-Storage Platform Abstract: With the rapid development of smart-grid, it is increasing for the proportion of new energy in the power system. The emergence of ...