Why real-time systems aren’t luxury infrastructure—they’re how smart businesses stay ahead. Let’s get one thing out of the way:Batch is fine—for laundry. Not for decisions. Most companies still move data the same way they moved it in 2005: extract, load, wait, analyze, repeat. It’s comfortable. It’s familiar. But it’s also a few hours—or days—behind what’s […]
Kafka Isn’t Just a Queue. And Flink Isn’t Just a Buzzword. Read More »
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
Implementing Real-Time Data Products with Apache Kafka and Apache Flink (Part 3) Read More »
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
Challenges in Building and Maintaining Data Products for AI and ML (Part 2) Read More »
I still remember the days in the early 2010s, when the term „big data“ was widely discussed in the tech industry. Companies were encouraged to collect as much data as possible, seeing it as a key resource (or the new oil as we called it those days) for the digital economy. However, focusing only on
What Are Data Products and Why Do They Matter for AI and ML? (Part 1) Read More »