Building AI agents is the first step, and it’s positive to see enterprises exploring this avenue. But it’s only the first step. For true enterprise value, these agents must seamlessly connect to your data ecosystem through robust integration, standardized protocols, and be guided by knowledgeable data teams. The need to give AI agents access to data and connect them to the necessary tools and functions has led to the creation of the Model Context Protocol (MCP).

Qlik Connect this year started a little differently. Not with slides. Not with a video. But with a few minutes of unified harmony. On stage stood the Barcelona Guitar Quartet — except instead of four guitars, there was only one. And yet the sound that emerged was rich, layered, and astonishing. If you had closed your eyes, you’d have sworn it was a full band. It was a masterclass in collaboration.

The future of data and analytics will be nothing like the experience we're used to today. We are at the beginning of a transformation that will fundamentally reshape how businesses use data, make decisions, and create value. At the center of this revolution is Agentic AI. Agentic AI fundamentally changes the way we work with data – moving from passive, reactive AI systems to autonomous, goal-oriented agents capable of reasoning, planning, and executing complex tasks across diverse data landscapes.

Open lakehouses powered by Apache Iceberg are redefining the landscape of data management. By bringing together the scalability of data lakes with the performance and reliability of data warehouses, lakehouses give you unprecedented flexibility and control over your data, while embracing open standards.

Global enterprises HubSpot, Saks, DocuSign, and Oldcastle Infrastructure modernized their data infrastructure on Fivetran and Snowflake, achieving AI-driven innovation, scalability, and millions in ROI.

BigQuery ML contribution analysis models, now GA, automate insight generation and output key change drivers from multidimensional data.

ClearML v3.25 introduces native support for vector databases within the Hyper-Datasets feature. This release enables users to store and search embeddings directly inside ClearML, opening the door to powerful custom RAG pipelines. In addition, v3.25 includes expanded orchestration metrics, new Application Gateway UI, and a range of UI upgrades to streamline day-to-day operations.