Data streaming is a new software category to process data in motion. Apache Kafka is the de facto standard used by over 100,000 organizations. Plenty of vendors offer Kafka platforms and cloud services. Many complementary stream processing engines like Apache Flink and SaaS offerings have emerged. And competitive technologies like Pulsar and Redpanda try to get market share. This blog post explores the data streaming landscape of 2023 to summarize existing solutions and market trends.
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.
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.
Not all workloads should go to the cloud! Low latency, cybersecurity, and cost-efficiency require a suitable combination of edge computing and cloud integration. This blog post explores hybrid data streaming with Apache Kafka anywhere. A live demo shows data synchronization from the edge to the public cloud across continents with Kafka on Hivecell edge hardware and serverless Confluent Cloud.
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.
This post explores why Apache Kafka is the new black for integration projects, how Kafka fits into the discussion around cloud-native iPaaS solutions, and why event streaming is a new software category. A concrete real-world example shows the difference between event streaming and traditional integration platforms respectively iPaaS.
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 5: Use cases and architectures for national security, cybersecurity, defense, and military.
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.