Kai Waehner

365 posts
bridging the gap between technical innovation and business value for real-time data streaming, processing and analytics
IT OT Convergence with Unified Namespace UNS and Data Product in Industrial IoT using Data Streaming Apache Kafka MQTT OPC UA
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  • 13 minute read

Unified Namespace vs. Data Product in IT/OT for Industrial IoT

Industrial companies are connecting machines, sensors, and enterprise systems like never before. Real-time data, cloud-native platforms, and AI are driving this transformation—but only if silos between OT and IT can be broken down. This blog introduces two key architecture patterns that support IT/OT convergence. The Unified Namespace structures live OT data, while Data Products govern and deliver that data across IT systems. Technologies like MQTT, OPC UA and Apache Kafka play a central role in building scalable, secure, and real-time data pipelines. Combined, these patterns enable clean integration, better data quality, and faster time to value—laying the foundation for success in manufacturing, energy, logistics, and beyond.
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Open RAN and Data Streaming with Apache Kafka and Flink in Telecom Industry for Network Analytics and Observability
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  • 8 minute read

Open RAN and Data Streaming: How the Telecom Industry Modernizes Network Infrastructure with Apache Kafka and Flink

Open RAN is transforming telecom by decoupling hardware and software to unlock flexibility, innovation, and cost savings. But to fully realize its potential, telcos need real-time data streaming for observability, automation, and AI. This post shows how Apache Kafka, Apache Flink, and a diskless data streaming platform like Confluent WarpStream help telco operators scale RAN data processing securely and cost-effectively.
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FinTech Alpian using Data Streaming and Agentic AI with Apache Kafka in Switzerland Regulated Market
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  • 6 minute read

Agentic AI and RAG in Regulated FinTech with Apache Kafka at Alpian Bank

Regulated FinTech is transforming financial services by combining compliance with innovation. This post explores how real-time data streaming with Apache Kafka and Flink enables modern architecture, personalization, and AI integration—while maintaining strict governance. Alpian, a fully licensed Swiss digital bank, showcases how Agentic AI, RAG, and domain-driven design work together in a compliant, cloud-only environment.
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Real Time AI for Mobile Gaming and eSports at MPL with Data Streaming using Apache Kafka and Flink
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  • 6 minute read

How MPL Uses Data Streaming to Lead in Mobile Gaming and eSports

Mobile Premier League (MPL) is a leading mobile eSports skill-based gaming platform with over 90 million users. To meet the demands of real-time personalization, fraud detection, and user engagement, MPL transitioned from traditional batch processing to a modern data streaming architecture powered by Apache Kafka, Apache Flink, and Confluent Cloud. This blog explores the architectural shift, key use cases—including real-time Machine Learning inference and feature orchestration—and the business outcomes achieved, such as increased trust, lower operational costs, and improved player retention. The MPL success story highlights the value of a data streaming platform in building responsive, intelligent, and secure gaming platforms.
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Mainframe Modernization and Integration with Data Streaming using Apache Kafka IBM MQ IIDR CDC Precisely Qlik
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  • 14 minute read

Mainframe Integration with Data Streaming: Architecture, Business Value, Real-World Success

The mainframe is evolving—not fading. With cloud-native features, AI acceleration, and quantum-safe encryption, platforms like IBM z16 and z17 remain central to critical industries. But modern demands require real-time data access and system agility. Apache Kafka and Flink make this possible by streaming data bi-directionally between DB2, IMS, and MQ and cloud analytics platforms. This enables event-driven architectures without disrupting core systems. This post outlines proven strategies—offloading, integration, and replacement—and includes real-world examples across industries. The result: lower costs, faster innovation, and smarter use of legacy systems.
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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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Durable Execution Engine with Restate Temporal DBOS vs Stream Processing with Kafka Streams Apache Flink Spark Structured Streaming
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  • 8 minute read

­­The Rise of the Durable Execution Engine (Temporal, Restate) in an Event-driven Architecture (Apache Kafka)

Durable execution engines like Temporal and Restate are redefining how developers orchestrate long-running, stateful workflows in distributed systems. Unlike traditional BPM tools focused on human-centric tasks, these engines automate machine-to-machine processes with built-in durability, retries, and fault-tolerant coordination. When integrated with event-driven platforms like Apache Kafka, they enable scalable, resilient architectures—handling complex business logic such as order processing, fraud detection, and multi-step transactions. This blog explores their capabilities, differences from stream processing tools like Apache Flink, Kafka Streams or Spark Structured Streaming, and the emerging role they play in modern enterprise infrastructure.
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How Penske Logistics Transforms Fleet Intelligence with Kafka and AI
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  • 7 minute read

How Penske Logistics Transforms Fleet Intelligence with Data Streaming and AI

Real-time visibility has become essential in logistics. As supply chains grow more complex, providers must shift from delayed, batch-based systems to event-driven architectures. Data Streaming technologies like Apache Kafka and Apache Flink enable this shift by allowing continuous processing of data from telematics, inventory systems, and customer interactions. Penske Logistics is leading the way—using Confluent’s platform to stream and process 190 million IoT messages daily. This powers predictive maintenance, faster roadside assistance, and higher fleet uptime. The result: smarter operations, improved service, and a scalable foundation for the future of logistics.
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Data Streaming with Confluent Meets SAP and Databricks for Agentic AI at Sapphire in Madrid
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  • 6 minute read

Data Streaming Meets the SAP Ecosystem and Databricks – Insights from SAP Sapphire Madrid

SAP Sapphire 2025 in Madrid brought together global SAP users, partners, and technology leaders to showcase the future of enterprise data strategy. Key themes included SAP’s Business Data Cloud (BDC) vision, Joule for Agentic AI, and the deepening SAP-Databricks partnership. A major topic throughout the event was the increasing need for real-time integration across SAP and non-SAP systems—highlighting the critical role of event-driven architectures and data streaming platforms like Confluent. This blog shares insights on how data streaming enhances SAP ecosystems, supports AI initiatives, and enables industry-specific use cases across transactional and analytical domains.
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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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