I am Jinja, a Business IT Coordinator.“The Sales Department and the Accounting Department have different addresses registered ...

In today’s evolving technological landscape, a solid understanding of the progression of data and application architecture is an important part of the technology leader’s role. By embracing modern ...

Confluent, an IBM company, today announced new capabilities for building a Streamhouse, a new type of data architecture that keeps business context current ...

Oracle’s Deep Trust AI Architecture moves AI agent security into the database. This article looks at the announcement, how it ...

Data scientist David King bridges the gap between unreliable data pipelines and scalable enterprise AI through production-ready data architecture and predictive modeling.

Learn how data sovereignty, local AI and hybrid architecture can help enterprises govern sensitive workloads, control costs and reduce cloud dependence.

TiDB is a prime example of an intrinsically scalable and reliable distributed SQL database architecture. Here’s how it works. In the good old days, databases had a relatively simple job: help with the ...

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 monthly overview of things you need to know as an architect or aspiring architect. Unlock the full InfoQ experience by logging in! Stay updated with your favorite authors and topics, engage with ...

Data fabric, data lakehouse, and data mesh have recently appeared as viable alternatives to the modern data warehouse. “It’s amazing to see some of these technologies come back around,” Serra said.

Modern data architecture is a hotbed for endless technological innovation and advancements that can best support enterprise business goals. While many organizations long for speedy, agile, scalable, ...

Many people believe data architecture and information architecture are one and the same. But this misconception among data management leaders and their teams results in poorly designed architectures ...