Systems | Development | Analytics | API | Testing

Healthcare is sitting on a paradox. As per healthcare analytics statistics 2026 It generates more data than any other industry, nearly 30 percent of the world’s total data, yet 97 percent of hospital data still goes unused. That gap is exactly where AI and machine learning in healthcare data analytics are changing the game. We are no longer talking about dashboards or retrospective reports.

The fastest way to answer a business question used to be expensive: an analyst on staff, a clean warehouse, a dashboard for every recurring ask. Then AI tools made the fast answer free for everyone. The analytics line item, oddly, did not shrink. Speed became free. Defensibility became the line item.

BigQuery Graph allows you to build a digital twin of your entire supply chain by turning your physical world—items, recipes, and locations—into a searchable map of nodes and edges, providing a new level of clarity.

Everyone wants an AI agent factory in 2026. Autonomous agents fixing bugs and shipping features while you sleep. I’ve been building toward that myself. But the error rates don’t support the fantasy. The best AI coding agents in the world fix about 50% of real bugs on SWE-bench verified. Half the time they fail. And AI-generated code produces 1.7x more issues than human-written code.

It's the day before your POC, and the embedded analytics demo still looks like it belongs to someone else. Your designer handed over a brand guide last week. Your developer has been buried in CSS variables ever since: cross-referencing token names, mapping changes across components, reloading the page after every tweak to see what broke. The UI is almost right. The nav color is close. The typography still isn't matching, but there's no time left.

For years, enterprise infrastructure treated security, storage, networking, and compute as mostly separate operational layers. Disruptive technologies tend to blur those lines. We saw this with backup and recovery, virtualization, and now with AI.

From dbt Core v2.0 to AI-powered connectors, Fivetran and dbt unveil joint product innovations that help data teams build open, scalable, AI-ready infrastructure.

See how Fivetran, dbt, and Google Cloud create the fresh, traceable, and well-governed data foundation AI agents need to deliver reliable business insights.

Last year, Nathan Hillyer's iOS platform engineering team at ForeFlight had self-hosted Mac hardware in their office, two engineers keeping them alive, and a codebase with over 2 million lines of Objective-C, Swift, and C++. Every Xcode update was a fire drill. Every capacity spike during a merge meant somebody was physically racking hardware in the Austin office. ForeFlight didn't want a new CI system. They wanted to stop being a data centre.

We are proud to announce the release of Apache Kafka 4.3. This release contains many new features and improvements. This blog post will highlight some of the more prominent ones. For a full list of changes, be sure to check the release notes. With 25 KIPs and over 600 commits since 4.2.0, this release introduces many new features, improvements and bug fixes to all the components. See the Upgrading to 4.3 section in the documentation for the list of notable changes and detailed upgrade steps.