Systems | Development | Analytics | API | Testing

Apache Iceberg has rapidly become the leading open table format for modern data lakehouses, enabling organizations to combine the cost-effectiveness of data lakes with the reliability and performance of data warehouses. However, successfully implementing and managing Iceberg tables at enterprise scale requires understanding both the technical fundamentals and the operational complexities involved. This guide walks through the essentials of working with Iceberg tables, from creation to optimization.

Your teams don’t buy features; they buy outcomes. Speed to insight, trusted AI, lower TCO. That’s the lens for our Qlik Product Recap, a curated view of what’s new across Qlik and why it matters. We loved meeting many of you at Qlik Connect, and you’ll see several features we highlighted there plus everything we’ve shipped since, organized by the outcomes they enable. While Qlik Connect is a great moment to showcase innovation, it’s only one waypoint.

Real-time analytics has become a cornerstone of modern enterprises. Businesses are no longer satisfied with waiting hours or days for insights—they demand answers in seconds. The rise of AI, machine learning, and generative AI has only accelerated this need, putting immense pressure on data platforms to deliver reliable, scalable, and flexible architectures.

In Europe, trust is everything, and the bar is set by law. GDPR, the AI Act, NIS2, DORA, and the Data Act shape how data and AI must operate. Leaders need to show where data lives, who can touch it, and how it moves, and they want cloud speed and flexibility without giving up control, so sovereignty and transparency must be built in from day one.

In a special session at Qlik Connect this past May in Orlando, attendees got to hear from three industry leaders - Dave Freriks from Qlik, Randy Pettus from Snowflake, and Sathisan Vannadil from AWS - all coming together to detail a cutting-edge integration of generative AI across the three different platforms. Their mission: help oil and gas companies harness the power of advanced data solutions to manage complexity, drive efficiency, and accelerate decision-making.

Does it sometimes feel like the world of AI is constantly resetting the clock? One day you’re under pressure to deliver value fast with your AI project. The next, a prominent study claims most projects deliver zero value. Then, a leading business publication explains that, actually, value doesn’t even matter right now. All while the technology itself is advancing at a dizzying pace.

Independent Software Vendors (ISVs) must continuously differentiate their offerings to thrive in today’s competitive SaaS market. Customers have high expectations and often carry a healthy dose of skepticism when a new vendor claims to deliver “the best solution” for productivity, cost savings, and revenue growth. To break through this status quo and deliver true value, ISVs need to provide smart, scalable, and secure solutions—faster than the competition.

Boost query performance by more than 2.5X and reduce storage costs by 50% compared to self-tuned Hive tables, without lifting a finger. Many data teams still need to manually develop and schedule custom tasks to maintain each and every table in their lakehouse, leading to inconsistent query performance and runaway costs.

The climate crisis is the defining challenge of our time, and cities are on the frontlines. Home to more than half of the world’s population, cities are both major contributors to global emissions and critical drivers of climate solutions. Delivering cleaner air, healthier communities, and resilient infrastructure requires not just commitment, but the power of data to guide and accelerate action.

A few months ago, at Qlik Connect, we announced the launch of Qlik Open Lakehouse, a new, fully managed capability that brings together the best of the Upsolver platform together with Qlik Talend Cloud. And today, we are excited to announce the general availability of Qlik Open Lakehouse within Qlik Talend Cloud that makes it easy and cost-effective for organizations to ingest, optimize, and manage massive amounts of data directly in Apache Iceberg–based lakehouses.