Browsing Tag

event streaming

39 posts
Event-Driven Agentic AI with Data Streaming using Apache Kafka and Flink
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  • 777 views
  • 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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The Importance of Focus for Software and Cloud Vendors - Data Streaming with Apache Kafka and Flink
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  • 801 views
  • 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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SaaS vs PaaS Cloud Service for Data Streaming with Apache Kafka and Flink
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  • 1.9K views
  • 6 minute read

Fully Managed (SaaS) vs. Partially Managed (PaaS) Cloud Services for Data Streaming with Kafka and Flink

The cloud revolution has reshaped how businesses deploy and manage data streaming with solutions like Apache Kafka and Flink. Distinctions between SaaS and PaaS models significantly impact scalability, cost, and operational complexity. Bring Your Own Cloud (BYOC) expands the options, giving businesses greater flexibility in cloud deployment. Misconceptions around terms like “serverless” highlight the need for deeper analysis to avoid marketing pitfalls. This blog explores deployment options, enabling informed decisions tailored to your data streaming needs.
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Lakehouse and Data Streaming - Competitor or Complementary
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  • 12 minute read

How Microsoft Fabric Lakehouse Complements Data Streaming (Apache Kafka, Flink, et al.)

In today’s data-driven world, understanding data at rest versus data in motion is crucial for businesses. Data streaming frameworks like Apache Kafka and Apache Flink enable real-time data processing. Meanwhile, lakehouses like Snowflake, Databricks, and Microsoft Fabric excel in long-term data storage and detailed analysis, perfect for reports and AI training. This blog post explores how these technologies complement each other in enterprise architecture.
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Data Lineage for Data Streaming with OpenLineage Apache Kafka and Flink
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  • 11 minute read

Open Standards for Data Lineage: OpenLineage for Batch AND Streaming

One of the greatest wishes of companies is end-to-end visibility in their operational and analytical workflows. Where does data come from? Where does it go? To whom am I giving access to? How can I track data quality issues? The capability to follow the data flow to answer these questions is called data lineage. This blog post explores market trends, efforts to provide an open standard with OpenLineage, and how data governance solutions from vendors such as IBM, Google, Confluent and Collibra help fulfil the enterprise-wide data governance needs of most companies, including data streaming technologies such as Apache Kafka and Flink.
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When NOT to use Apache Kafka
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  • 4.0K views
  • 3 minute read

When NOT to Use Apache Kafka? (Lightboard Video)

Apache Kafka is the de facto standard for data streaming to process data in motion. With its significant adoption growth across all industries, I get a very valid question every week: When NOT to use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How to qualify Kafka out as it is not the right tool for the job? This blog post contains a lightboard video that gives you a twenty-minute explanation of the DOs and DONTs.
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Data Streaming is not a Race it is a Journey
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  • 11 minute read

Data Streaming is not a Race, it is a Journey!

Data Streaming is not a race, it is a journey! Event-driven architectures and technologies like Apache Kafka or Apache Flink require a mind shift in architecting, developing, deploying, and monitoring applications. This blog post explores success stories from data streaming journeys across industries, including banking, retail, insurance, manufacturing, healthcare, energy & utilities, and software companies.
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The Real-Time Food and Retail Supply Chain powered by Apache Kafka
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  • 6.6K views
  • 9 minute read

Real-Time Supply Chain with Apache Kafka in the Food and Retail Industry

This blog post explores real-world deployments across the end-to-end supply chain powered by data streaming with Apache Kafka to improve business processes with real-time services. The examples include manufacturing, logistics, stores, delivery, restaurants, and other parts of the business. Case studies include Walmart, Albertsons, Instacart, Domino’s Pizza, Migros, and more.
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When not to use Apache Kafka
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  • 39.8K views
  • 18 minute read

When NOT to use Apache Kafka?

Apache Kafka is the de facto standard for event streaming to process data in motion. This blog post explores when NOT to use Apache Kafka. What use cases are not a good fit for Kafka? What limitations does Kafka have? How to qualify Kafka out as it is not the right tool for the job?
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