Big Data

Batch Is Fine—for Laundry. Not for Business Decisions

Kommentar verfassen / Stream Pipelines /

Expectations Have Changed—Permanently We live in a world that expects everything now. People track their parcels obsessively. They refresh flight apps every five minutes to check for gate changes. They want instant payment confirmations, real-time fraud checks, and status updates before they even think to ask. The modern customer is impatient—not because they’re difficult, but […]

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PhD Diaries: Research Isn’t What You Think

Kommentar verfassen / AI/ML /

When people hear I’m pursuing a PhD in Artificial Intelligence, the reactions are nearly predictable: “You must be incredibly smart,” or “Wow, working on the future of humanity?” The assumptions are flattering—but often far from accurate. The truth? Research in AI isn’t some linear march toward breakthrough innovation. It’s a nuanced, uncertain, often messy process

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Tech Is Only as Smart as the People Behind It

AI/ML /

Artificial Intelligence continues to redefine industries, promising automation, efficiency, and unprecedented insights. From self-driving cars to generative language models, AI is being positioned as a revolutionary force capable of transforming business and society. Yet, as impressive as these advancements are, there is one fundamental truth that often gets overlooked: technology is only as smart as

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Implementing Real-Time Data Products with Apache Kafka and Apache Flink (Part 3)

Architecture /

As we have explored in the previous parts of this series, high-quality and real-time data are essential for AI and ML applications. Now, let’s take a deeper look into how to implement real-time data products effectively using Apache Kafka and Apache Flink. This part focuses on two crucial features of Flink that enable reliable and

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Challenges in Building and Maintaining Data Products for AI and ML (Part 2)

Processing /

Building and maintaining data products for AI and ML is not just about collecting data—it is about ensuring data quality, scalability, and accessibility. Without addressing these challenges, AI models will produce unreliable results, and organizations will struggle to use data effectively. Two of the biggest challenges in this area are data quality and scalability. Ensuring

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GDPR Compliance with Apache Kafka: Handling Data Deletion Requests Effectively

Stream Pipelines /

Introduction The General Data Protection Regulation (GDPR), or Datenschutz-Grundverordnung (DSGVO) in German, mandates that organizations must be able to delete a user’s personal data upon request. Failure to comply with this requirement can result in severe fines, reaching up to 4% of a company’s global revenue or €20 million, whichever is higher. For companies using

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