Systems | Development | Analytics | API | Testing

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

Last week we publicly released Kai, our in-platform AI assistant, a data engineering agent that can build integrations, write transformations, debug failures, and document your entire project. I'm extremely proud of the team and what we've delivered. Yes, everyone has an AI assistant now. But most are chat wrappers that look great in scripted demos and fall apart with real work.

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

Today, we’re thrilled to announce that Koyeb has entered into a definitive agreement to join Mistral AI to advance cutting-edge AI infrastructure. Koyeb will bring its platform, technology, and team to accelerate Mistral Compute offering. Compute is designed to provide leading teams across the globe the same state-of-the-art infrastructure Mistral AI uses to build, run, and scale frontier models and AI software.

Creating AI writing tools is messier than you’d think. You start with this grand vision of an assistant that actually helps people write better, not just spits out generic text. Then reality hits. Models hallucinate. Users have wildly different needs. And suddenly you’re facing questions about responsibility, accuracy, and whether you’re building something genuinely useful or just another gimmick.

Modern applications depend on multiple services, APIs, databases, and third-party systems working together. While unit tests validate individual components, most production issues occur at integration points. That’s why integration testing tools are essential for ensuring system reliability. In this guide, we cover the top 10 integration testing tools for 2026, a quick comparison to help you choose the right one, and how these tools fit into modern CI/CD and DevOps workflows.

Gartner has named Qlik a Leader in the 2026 Gartner Magic Quadrant for Augmented Data Quality Solutions, our seventh time being recognized as a Leader in this Magic Quadrant. As AI becomes operational, data quality matters more than ever. We’re past the phase where AI just produces outputs. AI is starting to initiate, route, and act across real workflows.

Better specs and clearer task decomposition are a significant step forward. But specs and plans describe intentions. What AI agents also need is visibility into what systems actually do at runtime.

Heroku's logging system is your primary window into application behavior, but its ephemeral nature and streaming architecture can feel mysterious at first. This guide walks through everything developers need to know about Heroku logs, from understanding what they are and how to access them, to working around their limitations and forwarding them to external services like Honeybadger Insights for complete observability. Read on to master Heroku logging.

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.