Kai Wähner
- Global Field CTO, Kestra
Erlangen, Germany, Munich
Advisory Field CTO (own company) and Global Field CTO at Kestra. Author, speaker. Data integration, process intelligence, orchestration, trusted agentic AI.
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Articles by Kai Wähner
Kafka vs Flink vs Spark: Do You Really Need Real-Time?
Draft This article hasn't been published, so readers can't see it. In moderation This article is waiting for review, so readers can't see it yet. Deleted This article has been deleted, so readers can't see it. Join the DZone community Save articles, join the discussion, and download Refcards and Trend Reports with a free account. Real-time is the most overused word in data infrastructure. Every vendor in the Kafka vs Flink vs Spark debate leads with it. Milliseconds. Faster engines.
When to Use AMQP, JMS, Kafka, or MQTT: Trade-Offs, Not a Winner
Draft This article hasn't been published, so readers can't see it. In moderation This article is waiting for review, so readers can't see it yet. Deleted This article has been deleted, so readers can't see it. Join the DZone community Save articles, join the discussion, and download Refcards and Trend Reports with a free account. AMQP, JMS, Kafka, and MQTT get lined up as if you pick one and move on. Most of the time, that comparison is a category error.
MQTT, Kafka, NATS, or Zenoh? Choosing Edge-to-Cloud Messaging by Traffic Pattern
Draft This article hasn't been published, so readers can't see it. In moderation This article is waiting for review, so readers can't see it yet. Deleted This article has been deleted, so readers can't see it. Join the DZone community Save articles, join the discussion, and download Refcards and Trend Reports with a free account. Edge to cloud is not one integration problem. It is four: telemetry going up, control going down, sites syncing sideways, and data reaching people.
Complex Event Processing (CEP) With Apache Flink: What It Is and When (Not) to Use It
Join the DZone community and get the full member experience. Join For Free https://dzone.com/articles/apache-flink-1Most teams working with Apache Flink think about stream processing: filter events, aggregate values, enrich records, join streams. This covers the majority of real-time data problems. But there is a specific class of problems that stream processing handles poorly: detecting meaningful sequences of events across time.
YAML vs XML vs JSON: History, Trade-offs, and Where Each Wins in the Age of Agentic AI
Join the DZone community and get the full member experience. Join For Free Regularly, someone reopens the same argument. XML or JSON or YAML, as if one has to win and the others lose. It usually comes up in a context like data contracts, where a team has to pick a format and defend it. The framing is wrong. These formats were built for different jobs in different eras, and the more useful question is which one fits the job in front of you.
Why Databricks and Snowflake Speak the Kafka Protocol: Ingestion vs Architecture
Kafka-compatible ingestion is showing up everywhere now, including in analytics platforms that have nothing to do with running Kafka. Databricks added Kafka-compatible APIs to its Zerobus Ingest service. Snowflake went a step further at its Summit and announced Datastream, a native, fully Kafka-compatible streaming service. In both cases, existing Kafka producers can stream straight into the platform with no code changes, just a config change. This is good news for the ecosystem.
MCP vs REST/HTTP API vs Kafka: The Architect's Guide to Agentic AI Integration
Join the DZone community and get the full member experience. Join For Free Every major AI vendor now supports the Model Context Protocol. The framing is almost always the same: MCP is the universal connector for AI agents in the enterprise. That framing sets up a false choice. MCP, REST/HTTP APIs, and Apache Kafka are not alternatives. They solve different problems at different layers of the architecture.
The Trinity of Modern Data Architecture: Process Intelligence, Event-Driven Integration, and Trusted Agentic AI
Most enterprises have all three pieces. A process automation layer. A data integration strategy. An AI initiative. Yet critical decisions still fail, agents still hallucinate, and workflows still run on yesterday's data. The investments exist. The convergence does not. The problem is not a lack of technology, but a lack of architectural thinking.
dbt Meets Apache Flink: One Workflow for Data Engineers
Data engineers managing batch SQL pipelines on Snowflake, BigQuery, and increasingly Databricks, and streaming pipelines on Apache Flink face a familiar problem: two toolchains, two skill sets, two CI/CD pipelines.dbt is now extending into stream processing. This post explains what that means in practice, why it matters for data engineering teams, and what a concrete implementation looks like with Apache Flink on Confluent Cloud.
Stream Processing on the Mainframe With Apache Flink: Genius or a Glitch in the Matrix?
Running Apache Flink on a mainframe sounds odd at first. A modern stream processing engine on a platform most people call legacy? But take a closer look. It is not only possible. It might be a smart move for some of the largest financial institutions in the world. This post explores why some enterprises want Apache Flink on the mainframe, how it could work, and whether it is a brilliant innovation or a technical detour.
Dashboards and Queries for Apache Kafka
Join the DZone community and get the full member experience. Join For Free Dashboards are everywhere. Business and IT teams use them to track metrics, visualize trends, and make decisions. But when working with real-time data from Apache Kafka, it’s not obvious how to connect dashboards to the stream or whether you should at all. The conversation often jumps to technical options like Flink SQL, Kafka Streams Interactive Queries, or Confluent's TableFlow.
Cybersecurity with a Digital Twin: Why Real-Time Data Streaming Matters
Join the DZone community and get the full member experience. Join For Free Cyberattacks on critical infrastructure and manufacturing systems are growing in scale and sophistication. Industrial control systems, connected devices, and cloud services expand the attack surface far beyond traditional IT networks. Ransomware can stop production lines, and manipulated sensor data can destabilize energy grids. Defending against these threats requires more than static reports and delayed log analysis.
Scaling Kafka Consumers: Proxy vs. Client Library for High-Throughput Architectures
Join the DZone community and get the full member experience. Join For Free Apache Kafka is a powerful foundation for event-driven architectures. It enables true decoupling between producers and consumers. A single event can be consumed by multiple services, each evolving independently. The pull-based consumption model in Kafka is a key enabler of this flexibility, giving consumers full control over how and when to process data. This architecture works well in most deployments.
Online Feature Store for AI and Machine Learning with Apache Kafka and Flink
Real-time personalization has become a cornerstone of modern digital experiences. From content recommendations to dynamic user interfaces, delivering relevant interactions at the right moment depends on fresh data and fast machine learning inference. Traditional batch systems can’t keep up — especially when speed, scale, and accuracy are critical. A key component of the AI/ML architecture that enables this is the feature store.
Data Streaming for Real-Time Point-of-Sale (POS)
Point-of-Sale (POS) systems are no longer just cash registers. They are becoming real-time, connected platforms that handle payments, manage inventory, personalize customer experiences, and feed business intelligence. Small and medium-sized merchants can now access capabilities once reserved for enterprise retailers. Mobile payment platforms like Square, SumUp, and Shopify make it easy to sell anywhere and integrate sales channels seamlessly.
Global Payment Processors Scale with Apache Kafka and Flink
Join the DZone community and get the full member experience. Join For Free The recent announcement that Global Payments will acquire Worldpay for $22.7 billion has once again put the spotlight on the payment processing space. This move consolidates two giants and signals the growing importance of real-time, global payment infrastructure. But behind this shift is something deeper: data streaming has become the backbone of modern payment systems.
Future of Data Streaming with Apache Flink for Agentic AI
Join the DZone community and get the full member experience. Join For Free Agentic AI is changing how enterprises think about automation and intelligence. Agents are no longer reactive systems. They are goal-driven, context-aware, and capable of autonomous decision-making. But to operate effectively, agents must be connected to the real-time pulse of the business. This is where data streaming with Apache Kafka and Apache Flink becomes essential.
Beyond Brokers and Local Storage
Join the DZone community and get the full member experience. Join For Free Apache Kafka has come a long way from being just a scalable data ingestion layer for data lakes. Today, it is the backbone of real-time transactional applications. In many organizations, Kafka serves as the central nervous system connecting both operational and analytical workloads.
Multi-Region Apache Kafka using Synchronous Replication
Apache Kafka is the backbone of modern event-driven systems. It powers real-time use cases across industries. But deploying Kafka is not a one-size-fits-all decision. The right strategy depends on performance, compliance, and operational needs. From self-managed clusters to fully managed services and Bring-Your-Own-Cloud (BYOC) models, each approach offers different levels of control, simplicity, and scalability.
Agentic AI Using Apache Kafka as the Event Broker
Agentic AI is gaining traction as a design pattern for building more intelligent, autonomous, and collaborative systems. Unlike traditional task-based automation, agentic AI involves intelligent agents that operate independently, make contextual decisions, and collaborate with other agents or systems—across domains, departments, and even enterprises. In the enterprise world, agentic AI is more than just a technical concept; it is a transformative force.
The Top 20 Problems With Batch Processing
Batch processing has long been the default approach for moving and transforming data in enterprise systems. It works on fixed schedules, processes data in large chunks, and often relies on complex chains of jobs that run overnight. While this was acceptable in the past, today’s digital businesses operate in real time — and can’t afford to wait hours for fresh insights.
Real-Time Model Inference With Apache Kafka and Flink
Artificial intelligence (AI) and machine Learning (ML) are transforming business operations by enabling systems to learn from data and make intelligent decisions for predictive and generative AI use cases. Two essential components of AI/ML are model training and inference. Models are developed and refined using historical data. Model inference is the process of using trained machine learning models to make predictions or generate outputs based on new, unseen data.
KI-gestützte Cyberangriffe auf kritische Infrastrukturen - Abwehrstrategien
Stand: 20.11.2020 Es ist für uns eine Selbstverständlichkeit, dass wir verantwortungsvoll mit Ihren personenbezogenen Daten umgehen. Sofern wir personenbezogene Daten von Ihnen erheben, verarbeiten wir diese unter Beachtung der geltenden Datenschutzvorschriften. Detaillierte Informationen finden Sie in unserer Datenschutzerklärung. Ich bin damit einverstanden, dass die Vogel IT-Medien GmbH, Max-Josef-Metzger-Straße 21, 86157 Augsburg, einschließlich aller mit ihr im Sinne der §§ 15 ff.
Real-Time Fraud Detection in Mobility With Kafka and Flink
Join the DZone community and get the full member experience. Join For Free Mobility services like Uber, Grab, FREE NOW (Lyft), and DoorDash are built on real-time data. Every trip, delivery, and payment relies on accurate, instant decision-making. But as these services scale, they become prime targets for sophisticated fraud—GPS spoofing, fake accounts, payment abuse, and more. Traditional, batch-based fraud detection can’t keep up.
Open Industrial IoT Architectures with Data Streaming
Operational Technology (OT) has traditionally relied on legacy middleware to connect industrial systems, manage data flows, and integrate with enterprise IT. However, these monolithic, proprietary, and expensive middleware solutions struggle to keep up with real-time, scalable, and cloud-native architectures. Just as mainframe offloading modernized enterprise IT, offloading and replacing legacy OT middleware is the next wave of digital transformation.
Streaming Your Way Off Legacy With the Strangler Fig
Organizations looking to modernize legacy applications often face a high-stakes dilemma: Do they attempt a complete rewrite or find a more gradual, low-risk approach? Enter the Strangler Fig Pattern, a method that systematically replaces legacy components while keeping the existing system running.
Top Trends for Data Streaming with Apache Kafka and Flink
The evolution of data streaming has transformed modern business infrastructure, establishing real-time data processing as a critical asset across industries. At the forefront of this transformation, Apache Kafka and Apache Flink stand out as leading open-source frameworks that serve as the foundation for cloud services, enabling organizations to unlock the potential of real-time data.
Microsoft Fabric for Azure Cloud Beyond the Buzz)
If you ask your favorite large language model, Microsoft Fabric appears to be the ultimate solution for any data challenge you can imagine. That’s also the impression many people get from Microsoft’s sales teams. But is it really the silver bullet it’s made out to be? This article takes a closer look. The first part explores the glossy marketing and sales definition of the platform. The second part looks at Fabris's layers and deconstructs it from a more practical perspective.
SaaS vs. PaaS Cloud Services for Data Streaming
The cloud revolution has transformed how businesses deploy, scale, and manage data streaming solutions. While Software-as-a-Service (SaaS) and Platform-as-a-Service (PaaS) cloud models are often used interchangeably in marketing, their distinctions have significant implications for operational efficiency, cost, and scalability.
Stateless vs Stateful Stream Processing in Kafka and Flink
In data-driven applications, the rise of stream processing has changed how we handle and act on data. While traditional databases, data lakes, and warehouses are effective for many batch-based use cases, they fall short in scenarios demanding low latency, scalability, and real-time decision-making. This post explores the key concepts of stateless and stateful stream processing, using Kafka Streams and Apache Flink as examples.
Replace Visual Coding With Low-Code/No-Code Platforms
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 with Apache Kafka and Flink are reshaping the software and data engineering landscape. Low-code/no-code tools have been praised as transformative for software development and data engineering, providing visual interfaces that democratize technology access for non-technical users.
Bridging OT and IT With IIoT Middleware and Data Streaming
As industries continue to adopt digital transformation, the convergence of Operational Technology (OT) and Information Technology (IT) has become essential. The OT/IT Bridge is a key concept in industrial automation to connect real-time operational processes with business-oriented IT systems ensuring seamless data flow and coordination. This integration plays a critical role in the Industrial Internet of Things (IIoT).
Deployment Strategies for Apache Kafka Cluster Types
Join the DZone community and get the full member experience. Join For Free Organizations start their data streaming adoption with a single Apache Kafka cluster to deploy the first use cases. The need for group-wide data governance and security but different SLAs, latency, and infrastructure requirements introduce new Kafka clusters. Multiple Kafka clusters are the norm, not an exception. Use cases include hybrid integration, aggregation, migration, and disaster recovery.
The Open Table Format for Lakehouses and Data Streaming
Every data-driven organization has operational and analytical workloads. A best-of-breed approach emerges with various data platforms, including data streaming, data lake, data warehouse and lakehouse solutions, and cloud services.
Digitalization of Airport and Airlines With Kafka and Flink
The digitalization of airports faces challenges such as integrating diverse legacy systems, ensuring cybersecurity, and managing the vast amounts of data generated in real time. The vision for a digitalized airport includes seamless passenger experiences, optimized operations, consistent integration with airlines and retail stores, and enhanced security through the use of advanced technologies like IoT, AI, and real-time data analytics.
How I Trained a Chatbot of Myself Without Coding
Generative AI (GenAI) enables many new use cases for enterprises and private citizens. While I work on real-time enterprise-scale AI/ML deployments with data streaming, big data analytics, and cloud-native software applications in my daily business life, I also wanted to train a conversational chatbot for myself.
Shift Left: Batch, Lakehouse to Data Streaming
Data integration is a hard challenge in every enterprise. Batch processing and Reverse ETL are common practices in a data warehouse, data lake, or lakehouse. Data inconsistency, high compute costs, and stale information are the consequences. This blog post introduces a new design pattern to solve these problems: the Shift Left Architecture enables a data mesh with real-time data products to unify transactional and analytical workloads with Apache Kafka, Flink, and Iceberg.
Real-Time GenAI With RAG Using Apache Kafka, Flink
How do you prevent hallucinations from large language models (LLMs) in GenAI applications? LLMs need real-time, contextualized, and trustworthy data to generate the most reliable outputs. This blog post explains how RAG and a data streaming platform with Apache Kafka and Flink make that possible. A lightboard video shows how to build a context-specific real-time RAG architecture.
Open Standards for Data Lineage: OpenLineage
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.
Kafka, Flink, Snowflake: Cost-Efficient Analytics
Snowflake is a leading cloud data warehouse and transitions into a data cloud that enables various use cases. The major drawback of this evolution is the significantly growing cost of the data processing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables a "shift left architecture" where business teams can reduce cost, provide better data quality, and process data more efficiently.
Snowflake Integration Patterns
Snowflake is a leading cloud-native data warehouse. Integration patterns include batch data integration, Zero ETL, and near real-time data ingestion with Apache Kafka. This blog post explores the different approaches and discovers their trade-offs. Following industry recommendations, it is suggested to avoid anti-patterns like Reverse ETL and instead use data streaming to enhance the flexibility, scalability, and maintainability of enterprise architecture.
When Not to Choose Google Apache Kafka for BigQuery
Google announced its Apache Kafka for BigQuery cloud service at its conference Google Cloud Next 2024 in Las Vegas. Welcome to the data streaming club joining Amazon, Microsoft, IBM, Oracle, Confluent, and others. This blog post explores this new managed Kafka offering for GCP, reviews the current status of the data streaming landscape, and shares some criteria to evaluate when Kafka in general and Google Apache Kafka in particular should (not) be used.
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 should we not use Apache Kafka? What limitations does the event streaming platform have? When does Kafka simply not provide the needed capabilities? How do we qualify Kafka if it is not the right tool for the job?
The Past, Present, and Future of Stream Processing
Stream processing has existed for decades. However, it really kicks off in the 2020s thanks to the adoption of open-source frameworks like Apache Kafka and Flink. Fully managed cloud services make it easy to configure and deploy stream processing in a cloud-native way; even without the need to write any code. This blog post explores the past, present, and future of stream processing.
JavaScript, Node.js, Kafka: Full-Stack Data Stream
JavaScript is a pivotal technology for web applications. With the emergence of Node.js, JavaScript became relevant for both client-side and server-side development, enabling a full-stack development approach with a single programming language. Both Node.js and Apache Kafka are built around event-driven architectures, making them naturally compatible for real-time data streaming.
ARM CPU for Cost-Effective Apache Kafka
ARM CPUs often outperform x86 CPUs in scenarios requiring high energy efficiency and lower power consumption. These characteristics make ARM preferred for edge and cloud environments. This blog post discusses the benefits of using Apache Kafka alongside ARM CPUs for real-time data processing in edge and hybrid cloud setups, highlighting energy efficiency, cost-effectiveness, and versatility.
GenAI Demo With Kafka, Flink, LangChain and OpenAI
Generative AI (GenAI) enables automation and innovation across industries. This blog post explores the architecture and demo for the combination of Python, and LangChain with OpenAI LLM, Apache Kafka for event streaming and data integration, and Apache Flink for stream processing.
The Data Streaming Landscape 2024
The research company Forrester defines data streaming platforms as a new software category in a new Forrester Wave. Apache Kafka is the de facto standard used by over 100,000 organizations. Plenty of vendors offer Kafka platforms and cloud services. Many complementary open-source stream processing frameworks like Apache Flink and related cloud offerings emerged. Competitive technologies like Pulsar, Redpanda, or WarpStream try to get market share by leveraging the Kafka protocol.
MQTT Market Trends for 2024
The lightweight and open IoT messaging protocol MQTT has been adopted more widely across industries. This blog post explores relevant market trends for MQTT: cloud deployments and fully managed services, data governance with unified namespace and Sparkplug B, MQTT vs. OPC-UA debates, and the integration with Apache Kafka for OT/IT data processing in real-time. MQTT Summit in Munich In December 2023, I attended the MQTT Summit Connack. HiveMQ sponsored the event.
Why Tiered Storage for Apache Kafka Is a Big Thing
Apache Kafka added Tiered Storage to separate compute and storage. The capability enables more scalable, reliable, and cost-efficient enterprise architectures. This blog post explores the architecture, use cases, benefits, and a case study for storing Petabytes of data in the Kafka commit log. The end discusses why Tiered Storage does NOT replace other databases and how Apache Iceberg might change future Kafka architectures even more. Compute vs. Storage vs.
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