Systems | Development | Analytics | API | Testing

At Qlik, we’ve always done things a little differently. Maybe it’s our Swedish heritage. Maybe it’s our relentless curiosity. But for us, “different” doesn’t just apply to our platform — with its unmatched breadth and flexibility — or our people, who bring world-class guidance and support to every customer and partner we work with. It’s a mindset that runs through everything we do, including how we approach our biggest event of the year: Qlik Connect.

Keboola Data Templates are AI‑enhanced, reusable pipelines you launch with one click or API call. They eliminate the need to rebuild data workflows from scratch—so every use case is a fast-track to insights and impact. No more reinventing the wheel: once a template exists, it can be shared and adapted across departments—marketing, finance, ops—saving weeks of build time and hundreds of engineer-hours.

In today's data-driven landscape, organizations are inundated with vast amounts of information from diverse sources. To harness the full potential of this data, it's imperative to convert it into a consistent, usable format—a process facilitated by data conversion tools. These tools are integral to modern Extract, Transform, Load (ETL) workflows, enabling seamless data integration, transformation, and loading across various platforms.

Artificial Intelligence (AI) is no longer a futuristic ambition — it’s the strategic reality of today’s most competitive Independent Software Vendors (ISVs). In our recent Seamless Intelligence: Real-World AI Success Stories from Innovative ISVs webinar, I broke down how AI is transforming the ISV landscape, offering a blueprint for how organizations can move beyond dashboards, and into the age of predictive and generative intelligence.

Modern reporting tools like Tableau and Power BI have transformed how end users visualize and analyze data. But for developers and analysts relying on REST APIs to connect these platforms to their data sources, frustrations often mount. From inconsistent data formats to performance bottlenecks, REST APIs—while powerful—weren’t built to handle the complex, high-volume demands of modern BI environments.

The manufacturing industry can benefit from AI, data and machine learning to advance manufacturing quality and productivity, minimize waste and reduce costs. With ML, manufacturers can modernize their businesses through use cases like forecasting demand, optimizing scheduling, preventing malfunctioning and managing quality. These all significantly contribute to bottom line improvement.

Providing your analytics users the ability to get answers from their data is useful, but only if the solution can guide them to ask the right questions. The rising integration of artificial intelligence (AI) and machine learning (ML) in many business intelligence (BI) solutions has enabled new innovative approaches in combining natural language techniques with self-service analytics, resulting in helpful NLQ tools.

Just as some problems are too big for one person to solve, some tasks are too complex for a single artificial intelligence (AI) agent to handle. Instead, the best approach is to decompose problems into smaller, specialized units so that multiple agents can work together as a team. This is the foundation of a multi-agent system—networks of agents, each with a specific role, collaborating to solve larger problems. When building a multi-agent system, you need a way to coordinate how agents interact.