Enterprises racing to deploy generative AI often focus on models. In practice, outcomes depend on how well organizations ...

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...

Discover how industrial historians, data lakes, and time-series databases each play a critical role in transforming ...

Every data modernization effort starts with a blueprint. The architecture looks clean. The data flows are defined. The platform choice is justified. Whether it is a data warehouse, a data lake or a ...

The evolution of data architecture is accelerating. In 2025, 85% of DBTA subscribers reported plans to modernize their data platforms—driven largely by the explosive rise of GenAI and large language ...

Clinical data warehouses maximize veracity via schema-on-write and ACID guarantees, but ETL rework, limited modality support, ...

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 ...

Data modeling is the process of defining datapoints and struc­tures at a detailed or abstract level to communicate information about the data shape, content, and relationships to target audiences.

Over the past decade, the data boom has created exciting strategic opportunities for adaptive companies and enabled the development of entirely new enterprises. This wave was the result of 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 ...