Systems | Development | Analytics | API | Testing

Data visualization is one of the most important capabilities of any business intelligence (BI) and analytics solution. It helps people translate complex data into a visual context, like a chart or a graph, identify trends numbers alone can't easily reveal, and discover hidden patterns in your dashboard. Data visualization also provides a wealth of additional benefits for data analysis, such as enabling easier understanding of the correlation between operations and results.

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.

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.

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.

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.

Data is the new oil—but without a well-structured refinery, even the most abundant data becomes noise. Data warehouse modeling is that refinery: the critical blueprint that ensures organizations can store, retrieve, and analyze data with precision and efficiency. As of 2025, the landscape of data warehousing continues to evolve rapidly.

In 2025, the integration of Artificial Intelligence (AI) into Extract, Transform, Load (ETL) processes is transforming the data engineering landscape. Traditional ETL workflows are evolving from rigid, manually scripted pipelines into intelligent, adaptable systems powered by AI. These AI-driven ETL tools enable companies to handle increasing data complexity, schema drift, and real-time transformation demands without massive engineering overhead.