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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.

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.

From booking flows to subscription screens, formatting dates correctly is a core requirement in almost every Android app. In this post we’ll show you how to master the specific Android date format, understand its unique features and avoid common formatting pitfalls.

You're mid-conversation with an AI support agent. You've explained the problem, the agent is halfway through a response, and the connection drops. When you reconnect, the response is gone. You type the same question again. The agent asks the same clarifying questions again. Three minutes of context, gone. Not because the model forgot it, but because the delivery layer stored nothing.

Everyone is racing to put an AI agent on top of their data. Almost nobody is asking whether the agent can be trusted to act on what it sees. That is the wrong order. And the way most teams are trying to fix it — bigger context windows, more reasoning, another eval — is also wrong. The generative model stopped being the hard part of agentic analytics months ago. Wiring an LLM to a warehouse is a weekend project.