Systems | Development | Analytics | API | Testing

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.

As I recently transitioned to Qlik's Professional Services team to lead Advisory Services, I embraced a clear mission: to engage in high-level discussions with business stakeholders that drive faster value and enhance your return on investment (ROI). By employing a structured 10-step approach grounded in a repeatable agile methodology, we are actively enabling out customers to overcome challenges and achieve their strategic objectives.

Apache Parquet has become the de-facto standard for storing data used in analytics workloads, and has seen very broad adoption as a free and open-source storage format. When used as the underlying storage layer for Apache Iceberg, Parquet is also a foundational building block in modern lakehouse architectures, which enable warehouse-like capabilities on cost effective object storage. Let’s take a closer look at what Parquet actually is, and why it matters for big data storage and analytics.

Some data problems are universal — like dealing with unstructured data. At Bystronic, a global leader in sheet metal processing, we have mountains of it. From technical documentation to sales decks, HR policies, and IT knowledge bases, data is scattered across folders, servers, and systems. Industry research shows that 80% of enterprise data is unstructured, meaning it’s often invisible to the teams that need it most. As a result, up to 68% of that data goes unused. The impact is real.

Over the first half of this year, I’ve had the unique privilege of traveling across EMEA, APAC, and the US, leading our global Agentic AI workshop series. From London to Singapore to Mumbai, I’ve had a front-row seat to how enterprises—across industries and continents—are rethinking what software can be in an age of intelligent systems. And I can confidently say: the era of Agentic AI has arrived.