Browsing Tag

AI

15 posts
Learnings from the CIO Summit: AI + Data Streaming = Key for Success
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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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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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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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Data Streaming with Apache Kafka and Flink as Backbone for Real Time Cybersecurity at McAfee
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The Role of Data Streaming in McAfee’s Cybersecurity Evolution

In today’s digital landscape, cybersecurity faces mounting challenges from sophisticated threats like ransomware, phishing, and supply chain attacks. Traditional defenses like antivirus software are no longer sufficient, prompting the adoption of real-time, event-driven architectures powered by data streaming technologies like Apache Kafka and Flink. These platforms enable real-time threat detection, prevention, and response by processing massive amounts of security data from endpoints and systems. A success story from McAfee highlights how transitioning to an event-driven architecture with Kafka in Confluent Cloud has enhanced scalability, operational efficiency, and real-time protection for millions of devices. As cybersecurity threats evolve, data streaming proves essential for organizations aiming to secure their digital assets and maintain trust in an interconnected world.
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Data Streaming Trends for 2025 - Leading with Apache Kafka and Flink
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  • 18 minute read

Top Trends for Data Streaming with Apache Kafka and Flink in 2025

Apache Kafka and Apache Flink are leading open-source frameworks for data streaming that serve as the foundation for cloud services, enabling organizations to unlock the potential of real-time data. Over recent years, trends have shifted from batch-based data processing to real-time analytics, scalable cloud-native architectures, and improved data governance powered by these technologies. Looking ahead to 2025, the data streaming ecosystem is set to undergo even greater changes. Here are the top trends shaping the future of data streaming for businesses.
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Dynamic Pricing with Data Streaming using Apache Kafka and Flink
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  • 8 minute read

A New Era in Dynamic Pricing: Real-Time Data Streaming with Apache Kafka and Flink

In the age of digitization, the concept of pricing is no longer fixed or manual. Instead, companies increasingly use dynamic pricing — a flexible model that adjusts prices based on real-time market changes to enable real-time responsiveness, giving companies the tools they need to respond instantly to demand, competitor prices, and customer behaviors. This blog post explores the fundamentals of dynamic pricing, its link to data streaming, and real-world examples across different industries such as retail, logistics, gaming and the energy sector.
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Fraud Prevention with Apache Kafka in Real Time in Financial Services and Banking
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Fraud Prevention in Under 60 Seconds with Apache Kafka: How A Bank in Thailand is Leading the Charge

In financial services, the ability to prevent fraud in real-time is not just a competitive advantage – it is a necessity. For one of the largest banks in Thailand Krungsri (Bank of Ayudhya), with its vast assets, loans, and deposits, the challenge of fraud prevention has taken center stage. This blog post explores how the bank is leveraging data streaming with Apache Kafka to detect and block fraudulent transactions in under 60 seconds to ensure the safety and trust of its customers.
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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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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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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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