Real-time data beats slow data. It is that easy! But what is real-time? The term always needs to…
Data streaming emerged as a new software category. It complements traditional middleware, data warehouse, and data lakes. Apache Kafka became the de facto standard. New players enter the market because of Kafka’s success. One of those is Redpanda, a lightweight Kafka-compatible C++ implementation. This blog post explores the differences between Apache Kafka and Redpanda, when to choose which framework, and how the Kafka ecosystem, licensing, and community adoption impact a proper evaluation.
Fraud detection becomes increasingly challenging in a digital world across all industries. Real-time data processing with Apache Kafka became the de facto standard to correlate and prevent fraud continuously before it happens. This blog post explores case studies for fraud prevention from companies such as Paypal, Capital One, ING Bank, Grab, and Kakao Games that leverage stream processing technologies like Kafka Streams, KSQL, and Apache Flink.
Industrial IoT and Industry 4.0 enable digitalization and innovation. SCADA control systems are a vital component of IT/OT modernization. The SCADA evolution started with monolithic applications and moved to networked and web-based platforms. This blog post explores building the 5th generation: A cloud-native SCADA infrastructure with Apache Kafka. A real-world case study explores the journey of a German system operator for electricity to show how such a journey to open and scalable real-time workloads and edge-to-cloud integration progressed.
Logistics, shipping, and transportation require real-time information to build efficient applications and innovative business models. Data streaming enables correlated decisions, recommendations, and alerts. Kafka is everywhere across the industry. This blog post explores several real-world case studies from companies such as USPS, Swiss Post, Austrian Post, DHL, and Hermes. Use cases include cloud-native middleware modernization, track and trace, and predictive routing and ETA planning.
A modern supply chain requires just-in-time production, global logistics, and complex manufacturing processes. This blog post explores a solution that ingests all information flows into a unified central nervous system. The idea of the Supply Chain Control Tower becomes a reality: An integrated data cockpit with real-time access to all levels and systems of the supply chain.
The sports world is changing. Digitalization is everywhere. Cameras and sensors analyze matches. Stadiums get connected and incorporate mobile apps and location-based services. Players use social networks to influence and market themselves and consumer products. Real-time data processing is crucial for most innovative sports use cases. This blog post explores how data streaming with Apache Kafka helps reimagine the sports industry, showing a concrete example from the worldwide table tennis organization.
Apache Kafka became the de facto standard for data streaming. Various cloud offerings emerged and improved in the last years. Amazon MSK Serverless is the latest Kafka product from AWS. This blog post looks at its capabilities to explore how it relates to “the normal” partially managed Amazon MSK, when the serverless version is a good choice, and when other fully-managed cloud services like Confluent Cloud are the better option.
Request-response communication with REST / HTTP is simple, well understood, and supported by most technologies, products, and SaaS cloud services. Contrarily, data streaming with Apache Kafka is a fundamental change to process data continuously. HTTP and Kafka complement each other in various ways. This post explores the architectures and use cases to leverage request-response together with data streaming in the control plane for management or in the data plane for producing and consuming events.
If there were a buzzword of the hour, it would undoubtedly be “data mesh”! This new architectural paradigm unlocks analytic and transactional data at scale and enables rapid access to an ever-growing number of distributed domain datasets for various usage scenarios. The data mesh addresses the most common weaknesses of the traditional centralized data lake or data platform architecture. And the heart of a decentralized data mesh infrastructure must be real-time, reliable, and scalable. Learn how the de facto standard for data streaming, Apache Kafka, plays a crucial role in building a data mesh.