Kai Waehner

362 posts
bridging the gap between technical innovation and business value for real-time data streaming, processing and analytics
Real Time AI for Mobile Gaming and eSports at MPL with Data Streaming using Apache Kafka and Flink
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  • 209 views
  • 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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  • 775 views
  • 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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  • 16.5K views
  • 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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  • 2.2K views
  • 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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  • 6.5K views
  • 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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Real Time Gaming with Apache Kafka Powers Dream11 Fantasy Sports
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  • 1.1K views
  • 4 minute read

Powering Fantasy Sports at Scale: How Dream11 Uses Apache Kafka for Real-Time Gaming

Fantasy sports has evolved into a data-driven, real-time digital industry with high stakes and massive user engagement. At the heart of this transformation is Dream11, India’s leading fantasy sports platform, which relies on Apache Kafka to deliver instant updates, seamless gameplay, and trustworthy user experiences for over 230 million fans. This blog post explores how Dream11 leverages Kafka to meet extreme traffic demands, scale infrastructure efficiently, and maintain real-time responsiveness—even during the busiest moments of live sports.
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Data Streaming Lake Warehouse and Lakehouse with Confluent Databricks Snowflake using Iceberg and Tableflow Delta Lake
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  • 12 minute read

Databricks and Confluent Leading Data and AI Architectures – What About Snowflake, BigQuery, and Friends?

Confluent, Databricks, and Snowflake are trusted by thousands of enterprises to power critical workloads—each with a distinct focus: real-time streaming, large-scale analytics, and governed data sharing. Many customers use them in combination to build flexible, intelligent data architectures. This blog highlights how Erste Bank uses Confluent and Databricks to enable generative AI in customer service, while Siemens combines Confluent and Snowflake to optimize manufacturing and healthcare with a shift-left approach. Together, these examples show how a streaming-first foundation drives speed, scalability, and innovation across industries.
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Enterprise Application Integration with Confliuent and Databricks for Oracle SAP Salesforce Servicenow et al
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  • 1.9K views
  • 12 minute read

Databricks and Confluent in the World of Enterprise Software (with SAP as Example)

Enterprise data lives in complex ecosystems—SAP, Oracle, Salesforce, ServiceNow, IBM Mainframes, and more. This article explores how Confluent and Databricks integrate with SAP to bridge operational and analytical workloads in real time. It outlines architectural patterns, trade-offs, and use cases like supply chain optimization, predictive maintenance, and financial reporting, showing how modern data streaming unlocks agility, reuse, and AI-readiness across even the most SAP-centric environments.
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