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GenAI

13 posts
How OpenAI Uses Apache Kafka and Flink for GenAI and Agentic AI
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  • 5 minute read

How OpenAI uses Apache Kafka and Flink for GenAI

OpenAI revealed how it builds and scales the real-time data streaming infrastructure that powers its GenAI systems, including ChatGPT, at the Current 2025 conference in London. This blog post summarizes the role of Apache Kafka and Apache Flink in OpenAI’s architecture—enabling near-instant data processing, continuous feedback loops, and scalable coordination across model training and applications. From simplified Kafka consumption to multi-region Flink pipelines, OpenAI’s sessions showed why real-time data infrastructure is essential for both generative and agentic AI.
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Agentic AI with Apache Kafka as Event Broker Combined with MCP and A2A Protocol
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  • 7 minute read

Agentic AI with the Agent2Agent Protocol (A2A) and MCP using Apache Kafka as Event Broker

Agentic AI is emerging as a powerful pattern for building autonomous, intelligent, and collaborative systems. To move beyond isolated models and task-based automation, enterprises need a scalable integration architecture that supports real-time interaction, coordination, and decision-making across agents and services. This blog explores how the combination of Apache Kafka, Model Context Protocol (MCP), and Google’s Agent2Agent (A2A) protocol forms the foundation for Agentic AI in production. By replacing point-to-point APIs with event-driven communication as the integration layer, enterprises can achieve decoupling, flexibility, and observability—unlocking the full potential of AI agents in modern enterprise environments.
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Data Streaming and Lakehouse - Comparison of Confluent with Apache Kafka and Flink and Databricks with Spark
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  • 9 minute read

The Past, Present, and Future of Confluent (The Kafka Company) and Databricks (The Spark Company)

Confluent and Databricks have redefined modern data architectures, growing beyond their Kafka and Spark roots. Confluent drives real-time operational workloads; Databricks powers analytical and AI-driven applications. As operational and analytical boundaries blur, native integrations like Tableflow and Delta Lake unify streaming and batch processing across hybrid and multi-cloud environments. This blog explores the platforms’ evolution and how, together, they enable enterprises to build scalable, data-driven architectures. The Michelin success story shows how combining real-time data and AI unlocks innovation and resilience.
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Event-Driven Agentic AI with Data Streaming using Apache Kafka and Flink
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  • 12 minute read

How Apache Kafka and Flink Power Event-Driven Agentic AI in Real Time

Agentic AI marks a major evolution in artificial intelligence—shifting from passive analytics to autonomous, goal-driven systems capable of planning and executing complex tasks in real time. To function effectively, these intelligent agents require immediate access to consistent, trustworthy data. Traditional batch processing architectures fall short of this need, introducing delays, data staleness, and rigid workflows. This blog post explores why event-driven architecture (EDA)—powered by Apache Kafka and Apache Flink—is essential for building scalable, reliable, and adaptive AI systems. It introduces key concepts such as Model Context Protocol (MCP) and Google’s Agent-to-Agent (A2A) protocol, which are redefining interoperability and context management in multi-agent environments. Real-world use cases from finance, healthcare, manufacturing, and more illustrate how Kafka and Flink provide the real-time backbone needed for production-grade Agentic AI. The post also highlights why popular frameworks like LangChain and LlamaIndex must be complemented by robust streaming infrastructure to support stateful, event-driven AI at scale.
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How Data Streaming and AI Help Telcos - Top 5 Trends from MWC 2025
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  • 7 minute read

How Data Streaming and AI Help Telcos to Innovate: Top 5 Trends from MWC 2025

As the telecom and tech industries rapidly evolve, real-time data streaming is emerging as the backbone of digital transformation. For MWC 2025, McKinsey outlined five key trends defining the future: IT excellence, sustainability, 6G, generative AI, and AI-driven software development. This blog explores how data streaming powers each of these trends, enabling real-time observability, AI-driven automation, energy efficiency, ultra-low latency networks, and faster software innovation. From Dish Wireless’ cloud-native 5G network to Verizon’s edge AI deployments, leading companies are leveraging event-driven architectures to gain a competitive advantage. Whether you’re tackling network automation, sustainability challenges, or AI monetization, data streaming is the strategic enabler for 2025 and beyond. Read on to explore the latest use cases, industry insights, and how to future-proof your telecom strategy.
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Data Streaming with Apache Kafka and Flink vs Visual Coding with Low-Code No-Code
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  • 9 minute read

Why Generative AI and Data Streaming Are Replacing Visual Coding with Low-Code / No-Code Platforms

Low-code/no-code tools have revolutionized software development and data engineering by providing visual interfaces that empower non-technical users. However, their limitations in scalability, consistency, and integration pose significant challenges in modern, real-time architectures. Generative AI is emerging as a game-changer, offering unprecedented flexibility and customization, addressing many of the pitfalls of traditional low-code/no-code platforms. Simultaneously, the data ecosystem is evolving with Apache Kafka and Flink, enabling real-time, event-driven architectures that resolve inefficiencies of fragmented, batch-driven systems. This blog explores the evolution of low-code/no-code tools, their challenges, when (not) to use visual coding, and how generative AI and data streaming are reshaping the landscape.
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Real-Time AI ML Model Inference Predictive AI and Generative AI with Data Streaming using Apache Kafka and Flink
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  • 14 minute read

Real-Time Model Inference with Apache Kafka and Flink for Predictive AI and GenAI

Artificial Intelligence (AI) and Machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. This blog post explores how data streaming with Apache Kafka and Flink enhances the performance and reliability of model predictions. Whether for real-time fraud detection, smart customer service applications or predictive maintenance, understanding the value of data streaming for model inference is crucial for leveraging AI/ML effectively.
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How I Trained a Chatbot K.AI of Myself Without Coding Evaluating OpenAI Custom GPT Chatbase Botsonic LiveChatAI
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  • 13 minute read

Hello, K.AI – How I Trained a Chatbot of Myself Without Coding Evaluating OpenAI Custom GPT, Chatbase, Botsonic, LiveChatAI

Generative AI (GenAI) enables many new use cases for enterprises and private citizens. While I work on real-time enterprise scale AI/ML deployments with data streaming, big data analytics and cloud-native software applications in my daily business life, I also wanted to train a conversational chatbot for myself. This blog post introduces my journey without coding to train K.AI, a personal chatbot that can be used to learn in a conversational pace format about data streaming and the most successful use cases in this area. Yes, this is also based on my expertise, domain knowledge and opinion, which is available as  public internet data, like my hundreds of blog articles, LinkedIn shares, and YouTube videos.
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RAG and Kafka Flink to Prevent Hallucinations in GenAI
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  • 5 minute read

Real-Time GenAI with RAG using Apache Kafka and Flink to Prevent Hallucinations

How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. This blog post explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. A lightboard video shows how to build a context-specific real-time RAG architecture. Also, learn how the travel agency Expedia leverages data streaming with Generative AI using conversational chatbots to improve the customer experience and reduce the cost of service agents.
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GenAI Demo with Kafka, Flink, LangChain and OpenAI
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  • 10 minute read

GenAI Demo with Kafka, Flink, LangChain and OpenAI

Generative AI (GenAI) enables automation and innovation across industries. This blog post explores a simple but powerful architecture and demo for the combination of Python, and LangChain with OpenAI LLM, Apache Kafka for event streaming and data integration, and Apache Flink for stream processing. The use case shows how data streaming and GenAI help to correlate data from Salesforce CRM, searching for lead information in public datasets like Google and LinkedIn, and recommending ice-breaker conversations for sales reps.
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