Global Payment Processor Built with Data Streaming using Apache Kafka at Stripe Paypal Payoneer Worldline
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  • 10 minute read

How Global Payment Processors like Stripe and PayPal Use Data Streaming to Scale

This blog post explores how leading payment processors like Stripe, PayPal, Payoneer, and Worldline are leveraging data streaming with Apache Kafka to power real-time, scalable, and secure financial systems. As the industry shifts from batch processing to event-driven architecture, data streaming has become essential for handling transactions, fraud detection, compliance, and embedded services. The post highlights why Kafka is at the core of modern payment infrastructure – and how it enables innovation, resilience, and operational agility in the evolving fintech landscape.
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The Future of Data Streaming with Apache Flink for Agentic AI Supporting A2A and MCP
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The Future of Data Streaming with Apache Flink for Agentic AI

Agentic AI is moving into production. Autonomous, tool-using, goal-driven systems that need real-time data and context. Apache Kafka and Flink provide the event-driven foundation to run these agents at scale. With the new Flink Agents project (FLIP-531), Flink will natively support long-running, system-triggered AI agents integrated with LLMs, tools, and emerging protocols like MCP and A2A. This marks a major step toward reliable, enterprise-grade Agentic AI.
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The Rise of Diskless Apache Kafka with Object Storage and No Brokers
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The Rise of Diskless Kafka: Rethinking Brokers, Storage, and the Kafka Protocol

Apache Kafka has evolved from a data lake pipeline into the backbone of real-time transactional systems. The shift from broker-based storage to Tiered Storage and now to Diskless Kafka using cloud object storage redefines Kafka’s role. This blog explores the business value, technical architecture, and use cases of running Kafka without brokers, using the Kafka protocol as the foundation for scalable, cost-efficient event streaming.
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Synchronous Multi-Region Replication with Apache Kafka Confluent WarpStream for Zero Data Loss Disaster Recovery
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Multi-Region Kafka using Synchronous Replication for Disaster Recovery with Zero Data Loss (RPO=0)

Apache Kafka is the backbone of real-time data streaming. Choosing the right deployment model – self-managed, fully managed, or bring-your-own-cloud (BYOC) – is a strategic decision. It affects performance, compliance, and cost. This article explains the most common Kafka deployment strategies and highlights the innovation of synchronous multi-region replication to achieve zero data loss (RPO=0). Alternatives like stretched Kafka clusters, Confluent Multi-Region Clusters (MRC), and WarpStream offer different paths to RPO=0. They support critical workloads with strong durability and high availability. For mission critical and regulated use cases, zero data loss is no longer a future goal. It is now achievable with the right architecture.
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Data Streaming and AI in the Automotive Industry at OEMs like Porsche Tesla BMW
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Driving the Future: How Real-Time Data Streaming Is Powering Automotive Innovation

The automotive industry is rapidly shifting toward a software-defined, data-driven future. Real-time technologies like Apache Kafka and Apache Flink are now critical to powering connected vehicles, smart factories, autonomous platforms, and personalized mobility services. This blog explores how leading OEMs and suppliers use data streaming to drive digital transformation – from edge processing and AI to predictive maintenance and customer experience. As the industry moves toward intelligent, adaptive systems, event-driven architecture becomes a strategic foundation.
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Agentic AI with AWS Amazon Bedrock AgentCore and Data Streaming using Apache Kafka Flink and Confluent Cloud
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Building Agentic AI with Amazon Bedrock AgentCore and Data Streaming Using Apache Kafka and Flink

Agentic AI goes beyond chatbots. These are autonomous systems that observe, reason, and act—continuously and in real time. At AWS Summit New York 2025, Amazon launched Bedrock AgentCore to build and operate secure, scalable AI agents. But to run in production, agents also need real-time data, continuous context, and flexible integration. That’s where data streaming with Apache Kafka and Apache Flink comes in. Combined with open standards like MCP and A2A, they provide the event-driven foundation for always-on, enterprise-grade AI agents.
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FourKites Supply Chain Logistics Control Tower Powered by AI and Data Streaming with Confluent and Kafka
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Inside FourKites Logistics Platform: Data Streaming for AI and End-to-End Visibility in the Supply Chain

Global supply chains face constant disruption. Trade conflicts, wars, inflation, and shifting regulations are making logistics more unpredictable than ever. Traditional systems can’t keep up with the speed and complexity of today’s challenges. This blog post shows how FourKites, a leader in supply chain visibility, uses data streaming with Apache Kafka in the cloud, and AI to power real-time logistics. With over 3 million shipments tracked daily, FourKites delivers more than visibility — it enables fast, autonomous decisions at global scale.
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The Rise of Kappa Architecture in the Age of Agentic AI with Data Streaming using Apache Kafka and Flink
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The Rise of Kappa Architecture in the Era of Agentic AI and Data Streaming

The shift from Lambda to Kappa architecture reflects the growing demand for unified, real-time data pipelines that serve both analytical and operational needs. With the rise of Agentic AI and streaming-first systems, Kappa—powered by Apache Kafka and Apache Flink—delivers low-latency, event-driven infrastructure that supports modern applications, from scalable data products to autonomous AI agents. Open table formats and Shift Left principles further establish Kappa as the foundation for consistent, governed, and future-ready data platforms.
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Real-Time FinOps with Data Streaming using Apache Kafka and Flink
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FinOps in Real Time: How Data Streaming Transforms Cloud Cost Management

FinOps bridges the gap between finance and engineering to control cloud spend in real time. However, many organizations still rely on delayed, batch-driven data pipelines that limit visibility and slow down decisions. This blog explores how Apache Kafka and Apache Flink enable real-time, governed FinOps by streaming cloud usage data as it happens. It covers the challenges of data governance, compliance, and cross-functional accountability—and how streaming architecture addresses them. Real-world examples from Bitvavo and SumUp show how financial services companies scale securely, build cost-aware teams, and improve agility using event-driven platforms.
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