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.
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?
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.
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.
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.
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.
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.
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.
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.
Apache Kafka became the de facto standard for event streaming. Various vendors added Kafka and related tooling to their offerings or provide a Kafka cloud service. This blog post uses the car analogy – from the motor engine to the self-driving car – to explore the different Kafka offerings available on the market. The goal is not a feature-by-feature comparison. Instead, the intention is to educate about the different deployment models, product strategies, and trade-offs from the available options.