Systems | Development | Analytics | API | Testing

One year ago, we introduced Analyst Studio, ThoughtSpot’s unified workspace for preparing and managing AI-ready data, with a vision: to transform analysts from report generators into business catalysts. SQL, Python, and visual analysis finally worked together in one workspace, letting data teams move seamlessly between ad-hoc queries and advanced modeling, all while preparing data for the AI revolution we knew was coming.

Despite widespread awareness of data governance principles, 61% of organizations still list data quality as a top challenge. The gap between knowing what enterprise data management (EDM) requires and actually executing it continues to widen as data volumes explode and AI initiatives demand cleaner, more reliable information.

Every business decision relies on data—and bad data leads to bad decisions. ETL testing validates that your data extraction, transformation, and loading processes deliver accurate, complete, and consistent information to your analytics platforms. In 2026, the stakes have never been higher for organizations struggling with manual data validation that automated testing could eliminate.

If you've just enabled Kai in your Keboola project, you might be wondering: "Okay, now what?" This guide walks you through your first conversation with Kai, following the same steps I demonstrate in my introduction video.

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.

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.

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.

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.

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.