Systems | Development | Analytics | API | Testing

Explore what a modern data stack is, the modern data stack architecture, and examples of modern data stack tools that support scalable data integration.

Navigate to Workspaces → SQL Editor in your Keboola project. Write or paste your SQL query in the editor. ‍

In 2026, PropTech is no longer experimental. For most owners, operators, and investors, technology isn’t something you pilot — it’s core infrastructure. The shift is visible in both numbers and behavior. The global PropTech market is expected to grow from ~$34.4B in 2025 to ~$40.4B in 2026, with a projected ~17% CAGR through 2035. But growth alone doesn’t explain what’s happening.

AI-assisted software development makes building new features to help our customers an exercise in speed. Rainforest has a deep culture of experimentation and iteration, and we’re actively exploring how AI can help us work smarter. At our core, we aren’t just building an AI test generation tool; we are constantly experimenting with how AI can make our own development cycles leaner, faster, and more intuitive. That includes experimenting with AI-assisted software development.

In the age of AI, where delivery continues to accelerate, release confidence shouldn’t lag behind. Today’s software changes continuously, often generated or modified by AI. That raises complexity while shrinking the time quality teams have to plan, test, and make decisions. Manual workflows and static automation weren’t built for this pace.

In software development, load testing plays a critical role in ensuring that applications perform optimally under any imaginable load condition. To do this, developers subject applications to several types of load tests, including scalability, spike, endurance, and stress testing. The ultimate goal of these performance tests is to pinpoint potential bottlenecks and ensure the reliability of the overall system where the software application runs before reaching production.

Runtime validation does not replace static analysis. They solve different problems. Static analysis catches structural defects in code before it runs. Runtime validation catches behavioral failures by testing code against real production traffic. Enterprise teams adopting AI coding tools need both layers because AI-generated code introduces a new class of defects that neither layer catches alone.

This week, we had a chat with one of our enterprise customers about their thoughts on AI inside their Yellowfin environment. They’re currently running version 9.15 and use Yellowfin as the primary BI layer inside their app.

Discover how AI helps continuously test games 24/7, catch bugs earlier, and eliminate dev crunch.

Struggling with flaky tests and slow automation? Explore the best AI-powered testing tools of 2026 — and why Sauce Labs stands out.