Systems | Development | Analytics | API | Testing

A data lakehouse is an innovative data architecture that blends the strengths of data lakes and data warehouses into a single, cohesive system. It retains the cost-effectiveness and flexibility of data lakes while incorporating the structured data management and performance optimization capabilities of data warehouses.

When Elizabeth Barrett Browning wrote her love sonnets, she may not have been thinking about the largest ERP system in the world. Here at Qlik, SAP is just one of the many data sources we love, and we think about them every day. This blog is our sonnet to all the SAP endpoints we have made available for you when it comes to getting data out of SAP. Many SAP customers right now are full of fear, uncertainty and doubt with SAP moving the goalpost for accessing their data inside SAP systems.

AI and analytics are only as powerful as the data they rely on. Without accurate, consistent, and governed data, even the most advanced AI models can produce misleading results. That’s why data quality isn’t just nice to have — it’s essential. At Qlik, our customers across the globe rely on our solutions to drive AI-powered insights, optimize decision-making, and mitigate risk. And it all starts with trusted data.

The typical data engineer is balancing many priorities. In addition to the day-to-day tasks like pipeline construction, connecting sources, and establishing data trust and governance, pressure is increasing to contribute to the organization’s bottom line. This may happen by unlocking the full value of the organization’s data or devising ways to accelerate business outcomes. One way to accomplish both is through data products.

Some companies have created that unfair advantage by unlocking data’s hidden treasures, or by leveraging a powerful ecosystem, or by overcoming adversity, or through other great means like a unique company culture. Another great way is driving excellence in partner engagements to unlock value quicker than your competitors. So, what are the key ingredients for that particular recipe?

I recently saw a post on LinkedIn that said, “Qlik isn’t the same company it was in 2016.” I’m pretty sure that it wasn’t meant as a compliment. But here’s the thing: they’re right. And that’s a good thing. Because if we were the same company we were in 2016, we wouldn’t be prepared for the challenges businesses are facing today. The world of data and AI has changed. Businesses have changed. So, of course, Qlik has changed too.

In today’s data-driven world, organizations are increasingly turning to data products to gain actionable insights, improve decision making, and fuel innovation. Data products — productized datasets, metadata, and domain logic — are designed to democratize data access and make it easier for teams to maximize their data’s value without requiring deep technical expertise.

Tech headlines over the past week have been dominated by DeepSeek AI, which recently launched its groundbreaking R1 model. What’s most remarkable? The reported development cost—just under $6 million. This announcement challenges the long-held belief that creating advanced AI models requires astronomical investment, shaking the foundation of the tech industry and causing a ripple effect on global markets.

We are sprinkling a little Mary Poppins magic directly into Qlik Cloud Analytics with the introduction of Data Flow, our new no-code data prep designer. Imagine your data as a chaotic nursery, with spreadsheets strewn about like toys and insights hiding under the metaphorical bed. Enter Data Flow—your very own magical briefcase for the digital age. With a snap of YOUR fingers (or rather, a click of your mouse), it transforms your data disarray into a perfectly organized analytics wonderland.

Every year, January 28th marks International Data Privacy Day. This day is a timely reminder of the importance of safeguarding data and enabling trust. Even more so now we live in a world increasingly dominated by AI systems. For AI models to be accurate, they must be trained on massive amounts of data. The last thing you want however is for that data to unnecessarily include sensitive personal and confidential business information.