Partitioned Compute is a new feature in Fabric Dataflows that allows you to run certain operations inside a Dataflow query in parallel and therefore improve performance. While UI support is limited at ...

I have been spending a lot of time recently investigating the new performance-related features that have rolled out in Fabric Dataflows over the last few months, so expect a lot of blog posts on ...

SQL Server tools are used across industries to improve the performance, security, and automation of databases. Modern tools support cloud-hybrid s ...

Abstract: The 16T low-power SRAM is a specific type of memory cell designed to help minimize power consumption and maximize stability for energy-efficient uses. The new design also reduces leakage ...

OpenAI’s internal AI data agent searches 600 petabytes across 70,000 datasets, saving hours per query and offering a blueprint for enterprise AI agents.

Databricks, Snowflake, Amazon Redshift, Google BigQuery, and Microsoft Fabric – to see how they address rapidly evolving ...

Forbes contributors publish independent expert analyses and insights. Aytekin Tank is the founder and CEO of Jotform. If an entrepreneur builds a great product, but nobody hears about it, does it have ...

Heating accounts for nearly half of the global energy demand, and two-thirds of that is met by burning fossil fuels like natural gas, oil, and coal. Solar energy is a possible alternative, but while ...

Ever feel like creating custom columns in Power Query is more frustrating than it should be? The process can often feel overwhelming, especially when you’re juggling null values, complex calculations, ...

America’s AI boom is pushing the nation’s largest power-grid operator to the brink of a supply crisis. Sixty-seven million people in a 13-state region stretching from New Jersey to Kentucky get their ...

O n a Tuesday at the end of last semester, I asked my first-year writing class how they were using AI in their courses. One of my strongest students raised her hand. She admitted, almost sheepishly, ...