Systems | Development | Analytics | API | Testing

In Part 3 of this series, we discuss creating a dataset and have a first look at its governance and metatdata layers starting with the Dataset Overview. Stay tuned for more in the series as we continue building powerful Qlik data experiences!

Are you using datasets in Qlik Cloud Analytics yet? Before jumping into building a Data Product, there’s one foundational piece you can’t afford to overlook: the dataset. It’s more than just a data asset—it’s the backbone of everything you create in hashtag#Qlik. I just dropped a new video breaking down what a Qlik dataset really is, why it matters, and how to use it the right way before moving on to more advanced builds.

Discovery Agent brings automated anomaly detection directly into Qlik Cloud. It monitors the metrics you care about, identifies meaningful changes, and delivers contextual insights right where you work. Today it’s a monitoring capability inside Qlik Cloud. Over time, it becomes one of the agentic services supporting autonomous workflows across the platform.

Quickly see how to use Qlik Talend Data Integration to create a Replicate job to move data from YouTube Analytics to your analytics data store.

Discover how Qlik Answers transforms your data into clear, AI-powered insights in seconds. Ask questions in natural language and get accurate, contextual answers instantly.

Qlik Data Products for Analytics is how you turn raw data into something people can actually trust and reuse. It’s built right into Qlik Cloud Analytics and is designed for analytics teams, data producers, and even AI initiatives. Instead of everyone rebuilding datasets over and over, teams can publish curated, governed, analytics-ready data products that include business context, quality checks, and our patented Qlik Trust Score. People discover them in a marketplace, plug them straight into dashboards, apps, or AI workflows, and move fast with confidence. The big value? Less duplication, lower cost, faster app development, and insights you can actually trust.

See how to supercharge analytics development with Snowflake’s Cortex Code (CoCo) and Qlik’s Model Context Protocol (MCP). In this demo, we auto-generate master dimensions and measures for the Qlik app using AI—showing how Qlik and Snowflake work together to accelerate trusted analytics. What you’ll learn: Perfect for data engineers, BI developers, and analytics leaders who want to move from raw data to reliable insight—faster.

In this step-by-step, demo, we use AI to create master dimensions and master measures, then using Cortex and MCP automate the creation of the Qlik Sense app showing KPIs, tables, and filters—all powered by Qlik MCP & Snowflake Cortex Code. What you’ll learn: Perfect for BI developers, and analytics leaders who want trustworthy analytics. Watch now.

A quick demo of simple scenatio using Qlik MCP Server and Anthropic Claude as the LLM AI client. Where you should go next.