Learn the best practices for choosing a data model that suits your data needs and goals. Understand your data sources, requirements, options, architecture, and testing.

Learn how to create a scalable data model that can handle growth and complexity without compromising quality, efficiency, or usability in the context of data architecture.

defining the data architecture framework, standards, and principles, including modeling, metadata, security, reference data such as product codes and client categories, and master data such as ...

This general “move data from left to right” structure is commonly known as a multi-hop data architecture. The medallion architecture is likely the most popular form of the multi-hop architecture.

Index Terms—Data Architecture, Modeling Data Analytics Architecture, Big Data, IoT. Published in: 2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C) Date of Conference ...

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 expenses include hardware and software infrastructure, high electricity costs for model training, ... Despite these obstacles, we stand today on the cusp of a new era in data interaction.

In today’s data landscape, a distributed architecture is driven by the need for real-time insights, ... Regardless of size or operating model, this is not an all-or-nothing approach.

Modeling Interactions between Autonomous Agents in a Multi-Agent Self-Awareness Architecture Abstract: Learning from experience is a fundamental capability of intelligent agents. Autonomous systems ...