One key to efficient data analysis of big data is to do the computations where the data lives. In some cases, that means running R, Python, Java, or Scala programs in a database such as SQL Server or ...

Even as large language models have been making a splash with ChatGPT and its competitors, another incoming AI wave has been quietly emerging: large database models. Even as large language models have ...

Databases were traditionally highly specialized data stores that were designed for specific tasks and until recently, they've been getting even more specialized. Recall data warehouses? Somebody once ...

Occasionally one may hear that a data model is “over-normalized,” but just what does that mean? Normalization is intended to analyze the functional dependencies across a set of data. The goal is to ...

Enterprises are creating huge amounts of data and it is being generated, stored, accessed, and analyzed everywhere – in core datacenters, in the cloud distributed among various providers, at the edge, ...

As AI demand outpaces the availability of high-quality training data, synthetic data offers a path forward. We unpack how synthetic datasets help teams overcome data scarcity to build production-ready ...

As I described here, Power BI can send SQL queries in parallel in DirectQuery mode and you can see from the Timeline column there is some parallelism happening here – the last two SQL queries ...

Big data is less predictable than traditional data, and therefore requires special consideration when building models. Here are some things to keep in mind. Image: iStock/z_wei Data modeling is a ...