Apache Flink

Saga Pattern in Apache Flink: When It Works, When It Breaks, and What to Do Instead

Schreibe einen Kommentar / Architecture /

Someone came up to me at a conference and asked how they should implement the Saga pattern in Apache Flink and I think this is worth a written blogpost as well. Their team came from a classic microservices architecture, where Sagas had become the standard answer whenever one business transaction crossed several services. Now they […]

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Real-Time Data Is Real—Your Enterprise Roadmap Isn’t

Architecture, Data Steraming /

There’s a disconnect we don’t talk about enough.Data systems have gone real-time.Enterprise planning hasn’t. While Kafka pipes millions of events per second and Flink runs stateful computations in motion, most enterprises are still operating on a roadmap that looks like a spreadsheet and moves like a barge. This isn’t just a pace mismatch—it’s a mindset

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Kafka Isn’t Just a Queue. And Flink Isn’t Just a Buzzword.

Stream Pipelines, Architecture, Data Steraming /

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

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Batch Is Fine—for Laundry. Not for Business Decisions

1 Kommentar / 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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Real-Time Data Streaming with Kafka & Flink: The Foundation for AI and Modern Applications

2 Kommentare / Architecture /

In today’s digital world, real-time data streaming is no longer a luxury—it’s a necessity. Businesses and AI-driven applications thrive on instant insights, requiring robust data pipelines that can process and react to events as they happen. Apache Kafka and Apache Flink form the backbone of modern real-time data processing, enabling seamless data flow, low-latency analytics,

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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)

1 Kommentar / 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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Trade Monitoring and Pattern Matching with Flink and Kafka

Schreibe einen Kommentar / Processing, Stream Pipelines /

Financial markets generate one of the densest streams of real-time data we can observe today. Price ticks, order submissions, cancellations, executions, and settlement instructions all occur at millisecond scale. Within that torrent of activity, regulators and trading firms need to detect suspicious behavior: wash trades, spoofing, layering, or coordinated account activity. The traditional approach—batch analysis

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