The infrastructure service Cloudflare faced massive outages on Tuesday morning, cutting off access to ChatGPT, Claude, Spotify, X, and other platforms.

Today at Microsoft Ignite, we’re showcasing the next step in our evolution: Stack Overflow for Teams is now Stack Internal. It’s the next phase of our enterprise knowledge platform, reimagined for the AI era.

Picture this: You’re a data analyst on day one at a midsize SaaS company. You’ve got the beginnings of a data warehouse—some structured, usable data and plenty of raw data you’re not quite sure what to do with yet. But that’s not the real problem. The real problem is that different teams are doing their own thing: Finance has Power BI models loaded with custom DAX and Excel connections. Sales is using Tableau connected to the central data lake. Marketing has some bespoke solution you haven’t figured out yet. If you’ve worked in data for any number of years, this scene probably feels familiar.

Then a finance director emails: Why does ARR show as $250M in my dashboard when Sales just reported $275M in their call?

No problem, you think. You’re a data analyst; this is what you do. You start digging. What you find isn’t a simple calculation error. Finance and sales are using different date dimensions, so they’re measuring different time periods. Their definitions of what counts as “revenue” don’t match. Their business unit hierarchies are built on completely different logic: one buried in a Power BI model, the other hardcoded in a Tableau calculation. You trace the problem through layers of custom notebooks, dashboard formulas, and Excel workbooks and realize that creating a single version of the truth that’s governable, stable, and maintainable isn’t going to be easy. It might not even be possible without rebuilding half the company’s data infrastructure and achieving a level of compliance from other data users that would be a full-time job in itself.

This is where the semantic layer comes in—what VentureBeat has called the “$1 trillion AI problem.” Think of it as a universal translator for your data: It’s a single place where you define what your metrics mean, how they’re calculated, and who can access them. The semantic layer is software that sits between your data sources and your analytics tools, pulling in data from wherever it lives, adding critical business context (relationships, calculations, descriptions), and serving it to any downstream tool in a consistent format. The result? Secure, performant access that enables genuinely practical self-service analytics.

Why does this matter now? As we’ll see when we return to the ARR problem, one force is driving the urgency: AI.

Legacy BI tools were never built with AI in mind, creating two critical gaps. First, all the logic and calculations scattered across your Power BI models, Tableau workbooks, and Excel spreadsheets aren’t accessible to AI tools in any meaningful way. Second, the data itself lacks the business context AI needs to use it accurately. An LLM looking at raw database tables doesn’t know that “revenue” means different things to finance and sales, or why certain records should be excluded from ARR calculations.

The semantic layer solves both problems. It makes data more trustworthy across traditional BI tools like Tableau, Power BI, and Excel while also giving AI tools the context they need to work accurately. Initial research shows near 100% accuracy across a wide range of queries when pairing a semantic layer with an LLM, compared to much lower performance when connecting AI directly to a data warehouse.

So how does this actually work? Let’s return to the ARR dilemma.

The core problem: multiple versions of the truth. Sales has one definition of ARR; finance has another. Analysts caught in the middle spend days investigating, only to end up with “it depends” as their answer. Decision making grinds to a halt because no one knows which number to trust.

This is where the semantic layer delivers its biggest value: a single source for defining and storing metrics. Think of it as the authoritative dictionary for your company’s data. ARR gets one definition, one calculation, one source of truth all stored in the semantic layer and accessible to everyone who needs it.

You might be thinking, “Can’t I do this in my data warehouse or BI tool?” Technically, yes. But here’s what makes semantic layers different: modularity and context.

Once you define ARR in the semantic layer it becomes a modular, reusable object—any tool that connects to it can use that metric: Tableau, Power BI, Excel, your new AI chatbot, whatever. The metric carries its business context with it: what it means, how it’s calculated, who can access it, and why certain records are included or excluded. You’re not rebuilding the logic in each tool; you’re referencing a single, governed definition.

This creates three immediate wins:

• Single version of truth: Everyone uses the same ARR calculation, whether they’re in finance or sales, or they’re pulling it into a machine learning model.
• Effortless lineage: You can trace exactly where ARR is used across your organization and see its full calculation path.
• Change management that actually works: When your CFO decides next quarter that ARR should exclude trial customers, you update the definition once in the semantic layer. Every dashboard, report, and AI tool that uses ARR gets the update automatically. No hunting through dozens of Tableau workbooks, Power BI models, and Python notebooks to find every hardcoded calculation.

Which brings us to the second key function of a semantic layer: interoperability.

Back to our finance director and that ARR question. With a semantic layer in place, here’s what changes. She opens Excel and pulls ARR directly from the semantic layer: $265M. The sales VP opens his Tableau dashboard, connects to the same semantic layer, and sees $265M. Your company’s new AI chatbot? Someone asks, “What’s our Q3 ARR?” and it queries the semantic layer: $265M. Same metric, same calculation, same answer, regardless of the tool.

This is what makes semantic layers transformative. They sit between your data sources and every tool that needs to consume that data. Power BI, Tableau, Excel, Python notebooks, LLMs, the semantic layer doesn’t care. You define the metric once, and every tool can access it through standard APIs or protocols. No rebuilding the logic in DAX for Power BI, then again in Tableau’s calculation language, then again in Excel formulas, then again for your AI chatbot.

Before semantic layers, interoperability meant compromise. You’d pick one tool as the “source of truth” and force everyone to use it, or you’d accept that different teams would have slightly different numbers. Neither option scales. With a semantic layer, your finance team keeps Excel, your sales team keeps Tableau, your data scientists keep Python, and your executives can ask questions in plain English to an AI assistant. They all get the same answer because they’re all pulling from the same governed definition.

Back to day one. You’re still a data analyst at that SaaS company, but this time there’s a semantic layer in place.

The finance director emails, but the question is different: “Can we update ARR to include our new business unit?”

Without a semantic layer, this request means days of work: updating Power BI models, Tableau dashboards, Excel reports, and AI integrations one by one. Coordinating with other analysts to understand their implementations. Testing everything. Hoping nothing breaks.

With a semantic layer? You log in to your semantic layer software and see the ARR definition: the calculation, the source tables, every tool using it. You update the logic once to include the new business unit. Test it. Deploy it. Every downstream tool—Power BI, Tableau, Excel, the AI chatbot—instantly reflects the change.

What used to take days now takes hours. What used to require careful coordination across teams now happens in one place. The finance director gets her answer, sales sees the same number, and nobody’s reconciling spreadsheets at 5pm on Friday.

This is what analytics can be: consistent, flexible, and actually self-service. But getting there requires rethinking how we architect data systems. In the next article, “Evolving the Medallion: Data Architecture in the Era of Semantics,” we’ll explore how semantic layers change the way we think about data architecture.

At JetBrains, we love seeing the developer community grow and thrive. That’s why we support open-source projects that make a real difference — the ones that help developers learn, build, and create better software together. We’re proud to back open-source maintainers with free licenses and to contribute to initiatives that strengthen the ecosystem and the people behind it.

In this post, we highlight five open‑source projects from different ecosystems, written in established languages like Python and JavaScript or fast‑growing ones like Rust. Different as they are, each shares the same goal: elevating the developer experience. Together, they show how the right tools boost productivity and make workflows more enjoyable.

Ratatui

Born as the community-driven successor to the discontinued tui-rs library, Ratatui brings elegance to terminal UIs. It’s modular, ergonomic, and designed to help developers build interactive dashboards, widgets, and even embedded interfaces that go beyond the terminal.

JetBrains IDEs help me focus on the code rather than the tooling. They’re self-contained, so I don’t need to configure much to get started – they just work. With powerful code highlighting, automatic fixes, refactorings, and structural search, I can easily jump around the codebase and make edits.

— Orhun Parmaksız, Ratatui Core Maintainer

The upcoming 0.30.0 release focuses on modularity, splitting the main crate into smaller, independently usable packages. This change simplifies maintenance and makes it easier to use widgets in other contexts. And with new no_std support, Ratatui is expanding to power a wide range of use cases beyond the terminal.

Django

If Ratatui brings usability to the terminal, Django brings it to the web. Originally created in 2003 to meet both fast-paced newsroom deadlines and the demands of experienced developers, Django remains the go-to framework for “perfectionists with deadlines”. It eliminates repetitive tasks, enforces clean, pragmatic design, and provides built-in solutions for security, scalability, and database management – helping developers write less code and achieve more.

JetBrains IDEs, especially PyCharm, boost productivity with built-in Django support – including project templates, automatic settings detection, and model-to-database migrations – as well as integrated debugging and testing tools that simplify finding and fixing issues. The version control integration also makes it easier for contributors to refine and polish their work.

— Sarah Boyce, Django Fellow

Backed by a thriving global community, Django’s roadmap includes composite primary key support, built-in CSP integration, and a focus on making Django accessible by default. Every eight-month release delivers incremental improvements while maintaining backward compatibility – clear proof that long-term stability and innovation can coexist.

JHipster

Both Django and JHipster help developers move fast, but they take different paths. JHipster began as the “anti-mullet stack” – serious in the back, party in the front – created to help developers quickly bootstrap full-stack applications with Spring on the backend and Angular.js on the frontend. Today, it’s still one of the most comprehensive open-source generators, offering a complete full-stack solution with built-in security, performance, and best practices.

JHipster has always been about great productivity and great tooling, so naturally, we’ve always been IntelliJ IDEA fans – we even have our own JHipster IntelliJ IDEA plugin! What I love most is the clean UI, the performance, and all the plugins that make my life so much easier. I use Maven and Docker support all the time, and they’re both absolutely top-notch.

— Julien Dubois, JHipster Creator

The project is now split into two teams – JHipster Classic, which focuses on the original full-stack generator written in JavaScript, and JHipster Lite, which develops a modernized, DDD-oriented version written in Java and targeted primarily at the backend. This structure allows the community to experiment more freely and attract new contributors.

As AI-assisted generation evolves, JHipster’s mission remains the same: empowering developers with the latest cutting-edge technology and a true full-stack approach.

Biome

Once the structure is in place, consistency becomes the next challenge. That’s where Biome, a modern, all-in-one toolchain for maintaining web projects, comes in. It supports every major web language and maintains a consistent experience between the CLI and the editor. The goal of its creators was simple: make a tool that can handle everything from development to production, with fewer dependencies, less setup time, faster CI runs, and clear, helpful diagnostics.

I’m a long-term user of JetBrains IDEs! RustRover has greatly improved since launch – its debugging features and new JavaScript module mean I can maintain all Biome projects, even our Astro-based website, in a single IDE. It’s great that JetBrains really listens to users and their feedback.

— Emanuele Stoppa, Biome Creator

Biome’s roadmap includes adding Markdown support, type inference, .d.ts file generation, JSDoc support, and embedded-language support. As a community-led project, Biome welcomes contributions of all kinds – every bit of help makes a difference.

Vuestic UI

When it’s time to polish the frontend, Vuestic UI takes over. This open-source project focuses on accessibility, theming, and a delightful developer experience. Built for Vue 3, it offers a flexible, easy-to-use component library that scales effortlessly from quick prototypes to enterprise-grade dashboards.

The right development environment makes a huge difference when building complex open-source tools like Vuestic UI and Vuestic Admin. Our team relies on JetBrains IDEs every day for their best-in-class refactoring tools that let us make bold changes with confidence, fast and reliable code navigation, and rock-solid performance. Most of what we need works right out of the box – no extra plugins or setup required. For us, JetBrains isn’t just a preference – it’s a productivity multiplier.

— Maxim Kobetz, Senior Vue.js Developer

After 12 years in frontend development, WebStorm – along with IntelliJ IDEA and PyCharm – has always been my trusted toolkit. Even now, when I’m not coding every day, I know I can rely on WebStorm for quick tweaks – every update feels smooth and never disrupts my workflow. It’s intuitive, beautiful, and just works the way I expect it to.

I know switching IDEs is always a time sink, but with JetBrains, it’s absolutely worth it – you’ll never want to switch again.

— Anastasiia Zvenigorodskaia, Community Manager at Vuestic UI & Viuestic Admin

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These projects showcase a common truth: Great developer experience happens when tools get out of your way. With JetBrains IDEs enhancing everything from code navigation to collaboration, these teams turn ideas into usable, elegant tools.

Whether you’re crafting a command-line tool, prototyping a web service, or polishing a UI library, these tools highlight a shared goal – making development faster, more intuitive, and more enjoyable. Explore them, contribute if you can, and keep shaping the kind of developer experience you’d want for yourself.

SDU Show 93 features data expert Simon Sabin discussing data-related development, AI tools, and the upcoming SQLBits conference



Download audio: http://sqldownunder.blob.core.windows.net/podcasts/SDU93FullShow.mp3