Data models are used to represent real-world entities, but often have limitations. Avoid common data modeling mistakes for data integrity.

Data modeling is the framework that lets data analysis use data for decision-making. A combined approach is needed to maximize data insights.

The end goal of database design is to be able to transform a logical data model into an actual physical database. A logical data model is required before you can even begin to design a physical ...

A data modeling software helps craft a high-performance database, generate reports that can be useful for stakeholders and create data definition (a.k.a. data description) language (DDL). Good ...

This approach features a centralized database linked to other data stores with a common data model that carries information from one point to another, without the need to rewrite code.

This year is an expansive one for the database ecosystems that have evolved around the major platforms. Artificial intelligence (AI), machine learning, the Internet of Things (IoT), and cloud ...

Ted Hills hosted a workshop at the recent Data Architecture Summit 2018 Conference about data modeling for relational and NoSQL databases. He said that the NoSQL movement helped the database ...

Switzerland launched an open-source model called Apertus on Monday as an alternative to proprietary models like OpenAI’s ChatGPT or Anthropic’s Claude, reports SWI as spotted by Engadget. The model’s ...

With properly written applications taking advantage of the new data model, oaPartitions can customize how and what data will be loaded into memory from the database.

A novel approach from the Allen Institute for AI enables data to be removed from an artificial intelligence model even after it has already been used for training.