Data Streaming is not a Race it is a Journey
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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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Apache Camel vs Apache Kafka Comparison
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When to use Apache Camel vs. Apache Kafka?

Should I use Apache Camel or Apache Kafka for my next integration project? The question is very valid and comes up regularly. This blog post explores both open-source frameworks and explains the difference between application integration and event streaming. The comparison discusses when to use Kafka or Camel, when to combine them, when not to use them at all. A decision tree shows how you can quickly qualify out one for the other.
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When not to use Apache Kafka
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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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Top Apache Kafka Use Cases and Architectures for 2022
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Top 5 Apache Kafka Use Cases for 2022

Apache Kafka and Event Streaming are two of the most relevant buzzwords in tech these days. Ever wonder what my predicted TOP 5 Event Streaming Architectures and Use Cases for 2022 are to set data in motion? Check out the following presentation. Learn about the Kappa architecture, hyper-personalized omnichannel, multi-cloud deployments, edge analytics, and real-time cybersecurity.
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Stream Exchange for Data Sharing with Apache Kafka in a Data Mesh
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Streaming Data Exchange with Kafka and a Data Mesh in Motion

Data Mesh is a new architecture paradigm that gets a lot of buzzes these days. This blog post looks into this principle deeper to explore why no single technology is the perfect fit to build a  Data Mesh. Examples show why an open and scalable decentralized real-time platform like Apache Kafka is often the heart of the Data Mesh infrastructure, complemented by many other data platforms to solve business problems.
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Apache Kafka in the Public Sector for Smart City Infrastructure
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Apache Kafka in the Public Sector – Part 2: Smart City

The public sector includes many different areas. Some groups leverage cutting-edge technology, like military leverage. Others like the public administration are years or even decades behind. This blog series explores both edges to show how data in motion powered by Apache Kafka adds value for innovative new applications and modernizing legacy IT infrastructures. This is part 2: Use cases and architectures for a Smart City.
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Reverse ETL Anti Pattern vs Event Streaming with Apache Kafka
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When to Use Reverse ETL and when it is an Anti-Pattern

This blog post explores why software vendors (try to) introduce new solutions for Reverse ETL, when Reverse ETL is really needed, and how it fits into the enterprise architecture. The involvement of event streaming to process data in motion is a key piece of Reverse ETL for real-time use cases.
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Kappa Architecture vs Lambda Architecture for Apache Kafka Pulsar Data Lakes
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Kappa Architecture is Mainstream Replacing Lambda

Real-time data beats slow data. That’s true for almost every use case. Nevertheless, enterprise architects build new infrastructures with the Lambda architecture that includes separate batch and real-time layers. This blog post explores why a single real-time pipeline, called Kappa architecture, is the better fit. Real-world examples from companies such as Disney, Shopify, Uber, and Twitter explore the benefits of Kappa but also show how batch processing fits into this discussion positively without the need for Lambda.
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Apache Kafka in the Insurance Industry
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Apache Kafka in the Insurance Industry

The rise of data in motion in the insurance industry is visible across all lines of business including life, healthcare, travel, vehicle, and others. Apache Kafka changes how enterprises rethink data. This blog post explores use cases and architectures for insurance-related event streaming. Real-world examples from Generali, Centene, Humana, and Tesla show innovative data integration and stream processing in real-time.  
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De Facto Standard API - Amazon S3 for Object Storage and Apache Kafka for Event Streaming
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Kafka API is the De Facto Standard API for Event Streaming like Amazon S3 for Object Storage

Real-time beats slow data in most use cases across industries. The rise of event-driven architectures and data in motion powered by Apache Kafka enables enterprises to build real-time infrastructure and applications. This blog post explores why the Kafka API became the de facto standard API for event streaming like Amazon S3 for object storage, and the tradeoffs of these standards and corresponding frameworks, products, and cloud services.
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