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Apache Kafka

221 posts
Apache Kafka 4.0 - The Business Case for Data Streaming at Enterprise Scale
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  • 7 minute read

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
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Event-Driven Agentic AI with Data Streaming using Apache Kafka and Flink
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  • 12 minute read

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.
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Shift Left Architecture at Siemens with Stream Processing using Apache Kafka and Flink
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Shift Left Architecture at Siemens: Real-Time Innovation in Manufacturing and Logistics with Data Streaming

Industrial enterprises face increasing pressure to move faster, automate more, and adapt to constant change—without compromising reliability. Siemens Digital Industries addresses this challenge by combining real-time data streaming, modular design, and Shift Left principles to modernize manufacturing and logistics. This blog outlines how technologies like Apache Kafka, Apache Flink, and Confluent Cloud support scalable, event-driven architectures. A real-world example from Siemens’ Modular Intralogistics Platform illustrates how this approach improves data quality, system responsiveness, and operational agility.
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The Importance of Focus for Software and Cloud Vendors - Data Streaming with Apache Kafka and Flink
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  • 15 minute read

The Importance of Focus: Why Software Vendors Should Specialize Instead of Doing Everything (Example: Data Streaming)

As real-time technologies reshape IT architectures, software vendors face a critical decision: specialize deeply in one domain or build a broad, general-purpose stack. This blog examines why a focused approach—particularly in the world of data streaming—delivers greater innovation, scalability, and reliability. It compares leading platforms and strategies, from specialized providers like Confluent to generalist cloud ecosystems, and highlights the operational risks of fragmented tools. With data streaming emerging as its own software category, enterprises need clarity, consistency, and deep expertise. In this post, we argue that specialization—not breadth—is what powers mission-critical, real-time applications at global scale.
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  • 5 minute read

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.
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The Strangler Fig Design Pattern - Migration and Replacement of Legacy IT Applications with Data Streaming using Apache Kafka
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  • 8 minute read

Replacing Legacy Systems, One Step at a Time with Data Streaming: The Strangler Fig Approach

Modernizing legacy systems doesn’t have to mean a risky big-bang rewrite. This blog explores how the Strangler Fig Pattern, when combined with data streaming, enables gradual, low-risk transformation—unlocking real-time capabilities, reducing complexity, and supporting scalable, cloud-native architectures. Discover how leading organizations are using this approach to migrate at their own pace, stay compliant, and enable new business models. Plus, why Reverse ETL falls short and streaming is the future of IT modernization.
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Replacing OT Middleware with Data Streaming using Kafka and Flink for Cloud-Native Industrial IoT with MQTT and OPC-UA
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  • 11 minute read

Modernizing OT Middleware: The Shift to Open Industrial IoT Architectures with Data Streaming

Legacy OT middleware is struggling to keep up with real-time, scalable, and cloud-native demands. As industries shift toward event-driven architectures, companies are replacing vendor-locked, polling-based systems with Apache Kafka, MQTT, and OPC-UA for seamless OT-IT integration. Kafka serves as the central event backbone, MQTT enables lightweight device communication, and OPC-UA ensures secure industrial data exchange. This approach enhances real-time processing, predictive analytics, and AI-driven automation, reducing costs and unlocking scalable, future-proof architectures.
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Data Streaming with Apache Kafka and Flink as the Backbone for a B2B Data Marketplace
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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.
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Data Streaming with Apache Kafka and Flink in the Media Industry at Netflix Disney Plus Hotstar and Reliance JioCinema
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  • 8 minute read

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.
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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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