Browsing Tag

IBM

30 posts
Introduction Queues for Kafka - Apache Kafka QfK - One Platform for Event Streaming and Message Queues Consolidation
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  • 772 views
  • 10 minute read

When (Not) to Use Queues for Kafka?

Apache Kafka has long been the foundation for real-time data streaming. With the release of Queues for Kafka (QfK) in Apache Kafka 4.2, it now also supports native queuing, eliminating the need for separate message queue systems for backend integration and task processing. This blog explores how Kafka bridges the gap between stream processing and message queuing, when (not) to use QfK, and how it enables a unified cloud-native integration platform for modern enterprise architectures.
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Stream Processing on the IBM Mainframe with Apache Flink - Genius or a Glitch in the Matrix
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  • 6 minute read

Stream Processing on the Mainframe with Apache Flink: Genius or a Glitch in the Matrix?

Running Apache Flink on a mainframe may sound surprising, but it is already happening and for good reason. As modern mainframes like IBM z17 evolve to support Linux, Kubernetes, and AI workloads, they are becoming a powerful platform for real-time stream processing. This blog explores why enterprises are deploying Apache Flink on IBM LinuxONE, how it works in practice, and what business value it brings. With Kafka providing the data backbone, Flink enables intelligent processing close to where business-critical data lives. The result is a modern hybrid architecture that connects core systems with cloud-based innovation without needing to fully migrate off the mainframe.
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SaaS vs PaaS Cloud Service for Data Streaming with Apache Kafka and Flink
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  • 7.5K 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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Apache Flink - Overkill for Simple Stateless Stream Processing
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  • 8 minute read

Apache Flink: Overkill for Simple, Stateless Stream Processing and ETL?

Discover when Apache Flink is the right tool for your stream processing needs. Explore its role in stateful and stateless processing, the advantages of serverless Flink SaaS solutions like Confluent Cloud, and how it supports advanced analytics and real-time data integration together with Apache Kafka. Dive into the trade-offs, deployment options, and strategies for leveraging Flink effectively across cloud, on-premise, and edge environments, and when to use Kafka Streams or Single Message Transforms (SMT) within Kafka Connect for ETL instead of Flink.
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The Data Streaming Landscape 2025 with Kafka Flink Confluent Amazon MSK Cloudera Event Hubs and Other Platforms
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  • 21 minute read

The Data Streaming Landscape 2025

Data streaming is a new software category. It has grown from niche adoption to becoming a fundamental part of modern data architecture, leveraging open source technologies like Apache Kafka and Flink. With real-time data processing transforming industries, the ecosystem of tools, platforms, and cloud services has evolved significantly. This blog post explores the data streaming landscape of 2025, analyzing key players, trends, and market dynamics shaping this space.
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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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  • 31.8K views
  • 3 minute read

Apache Kafka vs. Middleware (MQ, ETL, ESB) – Slides + Video

This post shares a slide deck and video recording of the differences between an event-driven streaming platform like Apache Kafka and middleware like Message Queues (MQ), Extract-Transform-Load (ETL) and Enterprise Service Bus (ESB).
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