An advertising platform requires real-time capabilities to provide dynamic targeting, ad personalization, ad fraud detection, budget allocation, and event-driven marketing. This blog post explores how data streaming with Apache Kafka and Apache Flink enables context-specific advertising at any scale. Real-world success stories from Pinterest, Uber, Unity, buzzwil, and TV-Insight show different solutions and architectures for serving ads in marketing campaigns, embedded into mobile apps, and as SaaS software products.
Over 100,000 organizations use Apache Kafka for data streaming. However, there is a problem: The broad ecosystem lacks a mature client framework and managed cloud service for Python data engineers. Quix Streams is a new technology on the market trying to close this gap. This blog post discusses this Python library, its place in the Kafka ecosystem, and when to use it instead of Apache Flink or other Python- or SQL-based substitutes.
Apache Kafka and Apache Flink are increasingly joining forces to build innovative real-time stream processing applications. This blog post explores the benefits of combining both open-source frameworks, shows unique differentiators of Flink versus Kafka, and discusses when to use a Kafka-native streaming engine like Kafka Streams instead of Flink.
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.
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 3: Use cases and architectures for Government and Citizen Services.
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.
This blog series explores use cases and architectures for Apache Kafka in the cybersecurity space, including situational awareness, threat intelligence, forensics, air-gapped and zero trust environments, and SIEM / SOAR modernization. This post is part three: Cyber Threat Intelligence.
A smart city is an urban area that uses different types of electronic Internet of Things (IoT) sensors to collect data and then use insights gained from that data to manage assets, resources, and services efficiently. This blog post explores how Apache Kafka fits into the smart city architecture and the benefits and use cases.
Event Streaming with Apache Kafka disrupts the retail industry. Walmart’s real-time inventory system and Target’s omnichannel distribution and logistics are two great examples. This blog post explores a key use case for postmodern retail companies: Real-time omnichannel retail and customer 360.
Apache Kafka is NOT hard real-time in Industrial IoT or vehicles (such as autonomous cars) but integrates the OT/IT world for near real-time data correlation and analytics in hybrid architectures across factories at the edge, multiple clouds, and over countries.