AI initiatives don’t stall because models aren’t good enough, but because data architecture lags the requirements of agentic systems.

Whether you’re generating data from scratch or transforming sensitive production data, performant test data generators are critical tools for achieving compliance in development workflows.

With the official release of Microsoft's latest database offering, let's see what was improved and what still needs some work. Today, at Ignite, Microsoft announced the general availability of SQL ...

A comprehensive demonstration of data-diff functionality with a modern dbt project using dbt-duckdb. This project showcases how to detect and analyze data differences between development and ...

Abstract: For the evolution and maintenance of legacy systems, it is essential that they are reverse engineered. It is becoming important because in many Legacy systems, suitable documentation is not ...

Schema markup is a powerful tool for boosting your website’s SEO, yet many site owners overlook its potential. This comprehensive guide offers practical, real-world examples of how to effectively ...

As developers look to harness the power of AI in their applications, one of the most exciting advancements is the ability to enrich existing databases with semantic understanding through vector search ...

Organizations are using generative AI to stay ahead of the competition, but the real advantage lies in harnessing the power of your own data securely and at scale. SQL Server 2025, now in public ...

Abstract: Automated schema matching for multi-source heterogeneous databases can effectively promote data integration and interoperability, enhance data quality, support data migration and ...