Apache Kafka for Telco-OTT (Telecom Sector) and Media Applications
Kai Waehner
Current IT architectures in the telecom and media sector are not able to satisfy business needs because of…
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telecom
36 posts
Apache Kafka 4.0: The Business Case for Scaling Data Streaming Enterprise-Wide
Apache Kafka 4.0 represents a major milestone in the evolution of real-time data infrastructure. Used by over 150,000 organizations worldwide, Kafka has become the de facto standard for data streaming across industries. This article focuses on the business value of Kafka 4.0, highlighting how it enables operational efficiency, faster time-to-market, and architectural flexibility across cloud, on-premise, and edge environments. Rather than detailing technical improvements, it explores Kafka’s strategic role in modern data platforms, the growing data streaming ecosystem, and how enterprises can turn event-driven architecture into competitive advantage. Kafka is no longer just infrastructure—it’s a foundation for digital business
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.
The Top 20 Problems with Batch Processing (and How to Fix Them with Data Streaming)
Batch processing introduces delays, complexity, and data quality issues that modern businesses can no longer afford. This article outlines the most common problems with batch workflows—ranging from outdated insights to compliance risks—and illustrates each with real-world examples. It also highlights how real-time data streaming offers a more reliable, scalable, and future-proof alternative.
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.
Data Streaming as the Technical Foundation for a B2B Marketplace
A B2B data marketplace empowers businesses to exchange, monetize, and leverage real-time data through self-service platforms featuring subscription management, usage-based billing, and secure data sharing. Built on data streaming technologies like Apache Kafka and Flink, these marketplaces deliver scalable, event-driven architectures for seamless integration, real-time processing, and compliance. By exploring successful implementations like AppDirect, this post highlights how organizations can unlock new revenue streams and foster innovation with modern data marketplace solutions.
Data Streaming with Apache Kafka and Flink in the Media Industry: Disney+ Hotstar and JioCinema
The $8.5 billion merger of Disney+ Hotstar and Reliance’s JioCinema marks a transformative moment for India’s media industry, combining two of the most influential streaming platforms into a data streaming powerhouse. This blog explores how technologies like Apache Kafka and Flink power these platforms, enabling massive-scale content distribution, real-time analytics, and user engagement. With tools like MirrorMaker and Cluster Linking, the merger presents opportunities for seamless Kafka migrations, hybrid multi-cloud flexibility, and new innovations like multi-angle viewing and advanced personalization. The transparency of both platforms about their Kafka-based architectures highlights their technical leadership and the lessons they offer the data streaming community. The integration of their infrastructures sets the stage for redefining media streaming in India, offering exciting insights and benchmarks for organizations leveraging data streaming at scale.
Free Ebook: Data Streaming Use Cases and Industry Success Stories Featuring Apache Kafka and Flink
Real-time data is no longer optional—it’s essential. Businesses across industries use data streaming to power insights, optimize operations, and drive innovation. After 7+ years at Confluent, I’ve seen firsthand how Apache Kafka and Flink transform organizations. That’s why I wrote The Ultimate Data Streaming Guide: Concepts, Use Cases, Industry Stories—a free eBook packed with insights, real-world examples, and best practices. Download your free copy now and start your data streaming journey!
Why Generative AI and Data Streaming Are Replacing Visual Coding with Low-Code / No-Code Platforms
Low-code/no-code tools have revolutionized software development and data engineering by providing visual interfaces that empower non-technical users. However, their limitations in scalability, consistency, and integration pose significant challenges in modern, real-time architectures. Generative AI is emerging as a game-changer, offering unprecedented flexibility and customization, addressing many of the pitfalls of traditional low-code/no-code platforms. Simultaneously, the data ecosystem is evolving with Apache Kafka and Flink, enabling real-time, event-driven architectures that resolve inefficiencies of fragmented, batch-driven systems. This blog explores the evolution of low-code/no-code tools, their challenges, when (not) to use visual coding, and how generative AI and data streaming are reshaping the landscape.
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.
