This is Part 1 of a two-part video series exploring how Qlik MCP tools can be used with declarative pipelines directly within VS Code. In this video, I demonstrate how to use the MCP tools to explore and validate your Qlik environment before building anything—identifying available connections, inspecting source tables, reviewing pipeline project information, and verifying the resources you plan to use.

This is Part 2 of a two-part video series exploring how Qlik MCP tools can be used with declarative pipelines directly within VS Code. In this video, Mike Tarallo, demonstrates how to now create the data pipeline using declarative YAML in VS Code. You will see the creation process, learn some tips and tricks and see the final result.

In this video, learn how to set up AgentSpot's self serve free tier from scratch. You'll connect the apps you already work with (Google Workspace, Slack, and more) and authenticate them in a couple of clicks. Plus, you'll build your first agent, one that catches you up on Slack threads so you don't have to. Tip: the more tools you connect, the more context your agent has, and the better it works for you.

AI Optimization is a workspace for managing how Qlik Answers understands an application. It brings semantic management into one experience, where you can review AI-generated semantic understanding and make corrections before they reach an answer. The result is a visible, correctable layer where there used to be none. AI Optimization is the central place to manage how Qlik Answers interprets your application. Semantic Understanding, inside it, shows and lets you edit this interpretation of each field and master item.

Our own sales team was losing up to 2 hours per account just switching between tools. Here's how they got that time back. In this video, our SVP of Revenue Strategy walks through how her team built a single sales hub with AgentSpot, pulling data from ThoughtSpot, Salesforce, Gong, and external intent signals into one workflow. What you'll see: What's hot in your territory: the accounts showing real buying intent, based on search trends and ICP fit.

How does our sales and revenue operation teams use Spotter, our agentic analyst, to instantly access and analyze their data using natural language? In this video, you'll see how Spotter makes data interrogation easy and reliable. Learn how to: Explore Data Sets: Quickly understand what data you have access to, where it is pulling from, and get immediate examples of what you can ask. Get Instant Answers: Type a natural language question (like "What's the booked ACV this quarter by region?") and instantly receive easy-to-read charts and deep analyses.

As enterprise AI scales, traditional data architectures are reaching their limit. Workloads are becoming more distributed, data movement is accelerating, and tech leaders face growing pressure to justify AI spend while delivering real business impact. Chief Technology Officer Sergio Gago breaks down key findings from Cloudera’s latest global survey of enterprise architects, data architects, and cloud leaders.