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Artificial Intelligence

10 posts
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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Learnings from the CIO Summit: AI + Data Streaming = Key for Success
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  • 5 minute read

CIO Summit: The State of AI and Why Data Streaming is Key for Success

The CIO Summit in Amsterdam provided a valuable perspective on the state of AI adoption across industries. While enthusiasm for AI remains high, organizations are grappling with the challenge of turning potential into tangible business outcomes. Key discussions centered on distinguishing hype from real value, the importance of high-quality and real-time data, and the role of automation in preparing businesses for AI integration. A recurring theme was that AI is not a standalone solution—it must be supported by a strong data foundation, clear ROI objectives, and a strategic approach. As AI continues to evolve toward more autonomous, agentic systems, data streaming will play a critical role in ensuring AI models remain relevant, context-aware, and actionable in real time.
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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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Online Model Training and Model Drift in Machine Learning with Apache Kafka and Flink
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  • 5 minute read

Online Model Training and Model Drift in Machine Learning with Apache Kafka and Flink

The rise of real-time AI and machine learning is reshaping the competitive landscape. Traditional batch-trained models struggle with model drift, leading to inaccurate predictions and missed opportunities. Platforms like Apache Kafka and Apache Flink enable continuous model training and real-time inference, ensuring up-to-date, high-accuracy predictions. This blog explores TikTok’s groundbreaking AI architecture, its use of data streaming for real-time recommendations, and how businesses can leverage Kafka and Flink to modernize their ML pipelines. I also examine how data streaming complements platforms like Databricks, Snowflake, and Microsoft Fabric to create scalable, adaptive AI systems.
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Top 10 Real Time Innovations in FinServ with Data Streaming using Apache Kafka and Flink
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  • 10 minute read

How Data Streaming with Apache Kafka and Flink Drives the Top 10 Innovations in FinServ

The financial industry is rapidly shifting toward real-time, intelligent, and seamlessly integrated services. From IoT payments and AI-driven banking to embedded finance and RegTech, financial institutions must process vast amounts of data instantly and securely. Data Streaming with Apache Kafka and Apache Flink provides the backbone for real-time payments, fraud detection, personalized financial insights, and compliance automation. This blog post explores the top 10 emerging financial technologies and how data streaming enables them, helping banks, fintechs, and central institutions stay ahead in the future of finance.
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Stateless and Stateful Stream Processing with Kafka Streams and Apache Flink
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  • 8 minute read

Stateless vs. Stateful Stream Processing with Kafka Streams and Apache Flink

The rise of stream processing has changed how we handle and act on data. While traditional databases, data lakes, and warehouses are effective for many batch-based use cases, they fall short in scenarios demanding low latency, scalability, and real-time decision-making. This post explores the key concepts of stateless and stateful stream processing, using Kafka Streams and Apache Flink as examples.
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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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The Past Present and Future of Stream Processing
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  • 12 minute read

The Past, Present and Future of Stream Processing

Stream processing has existed for decades. The adoption grows with open source frameworks like Apache Kafka and Flink in combination with fully managed cloud services. This blog post explores the past, present and future of stream processing, including the relation of machine learning and GenAI, streaming databases, and the integration between data streaming and data lakes with Apache Iceberg.
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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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