Systems | Development | Analytics | API | Testing

IT Professionals Day is our opportunity to celebrate the people who keep the digital world running - the ones who make sure data is secure, reliable, and ready to power innovation. At Qlik, we know IT professionals aren’t just solving problems, they’re enabling possibilities.

It’s been nearly three years since generative AI burst onto the scene, promising to revolutionize decision-making and operations for enterprises. The early days were all about visions and roadmaps; but now, boards and CFOs are demanding real ROI and measurable business impact from their investments.

We’re at a pivotal moment in the data integration market. For years, the rallying cry for data professionals was to adopt the "modern data stack" (MDS), with the promise of a "best-of-breed" approach. This vision led many organizations to assemble a collection of specialized, single-purpose tools—one for ingestion, another for transformation, a third for quality, and so on—to create a highly effective data environment. However, reality has proven far more complex.

Qlik’s partnership with Amazon Web Services (AWS) is at the very core of our cloud data integration and analytics platform. Think of it like the iconic double helix of DNA. Just as DNA’s intertwined strands carry the fundamental instructions for life, the interconnection between Qlik and AWS carries the innovation and reliability that power the future of enterprise analytics, data integration, and AI.

When comparing Apache Parquet and Apache Iceberg, we need to first understand the differences between file formats and table formats. Parquet is a file format, whereas Iceberg is a table format. File formats focus on efficient storage and compression of data. They define how the raw bytes representing records and columns are organized and encoded on disk or in a distributed file system such as Amazon S3. The Parquet file format has become the de-facto standard for storing data used in analytics workloads.

Apache Iceberg is transforming how organizations build and manage their data infrastructure, enabling lakehouse architectures that combine the best of data lakes and data warehouses. In this blog, we look at five real-world implementations demonstrate Iceberg's versatility and the advantages it brings to modern data management challenges. Learn more about Data Lakehouses.

Data is supposed to be the lifeblood of innovation in today’s enterprises. We’re told to be data-driven, yet many organizations find themselves submerged in a digital swamp — murky, unstructured, and increasingly hard to navigate. Without a unified data management platform, data scale can quickly spiral into chaos — not because of technology limitations, but due to how data is handled, shared, and reused across teams.

With growing data volumes, organizations are forced to rethink how they store and manage data. Traditional data warehouses, while powerful, became expensive and rigid when faced with the volume, variety, and velocity of modern data - leading to the rise of data lakes as a promising alternative. However, organizations soon found that data lake in themselves were not a panacea, and often provided limited utility due to their unstructured nature.

I had the opportunity to attend the AWS Summit New York 2025 at the iconic Jacob Javits Center in July. The event brought together thousands of cloud enthusiasts, developers, and business leaders to explore the latest in generative AI, cloud innovation, and real-world applications across industries. From major announcements and product launches to immersive sessions and after-hours networking, the Summit delivered both inspiration and insight.

Data warehouses have long been the gold standard for data analysis and reporting. Developed in the 1980s, the data warehouse concept was introduced to provide an architectural model for data flows from operational systems to decision support systems.