Stream Processing, simple et serverless

"To meet rising customer demands in a volatile energy market, we need to deliver near real-time data to our client-facing applications. Relying on batch processing can cause performance issues and result in poor decision-making based on outdated data. By using Kafka and Flink together in a unified platform, our teams will be able to easily build intelligent streaming data pipelines that can extract data from various sources, process it in real time, and feed it to our downstream consumers for timely analysis without any operational challenges. We’re excited about Confluent’s fully managed Flink service, because it will help make stream processing accessible to everyone by creating high quality, reusable data streams to fuel innovation and data exploration across our lines of business.”

"Conditions in the automotive logistics industry can change rapidly, requiring immediate action to address delays, reroute vehicles, and update systems and customers. Confluent's serverless Flink service will enable us to instantly and efficiently transform, integrate, and enrich massive volumes of data in our transportation management system, providing real-time visibility into the status and location of vehicles for both systems and customers."

"Apache Flink is becoming a prominent stream processing framework in this shift towards real-time insights. Flink and Apache Kafka are commonly used together for real-time data processing, but differing data formats and inconsistent schemas can cause integration challenges and hinder the quality of streaming data for downstream systems and consumers. A fully managed, unified Kafka and Flink platform with integrated monitoring, security, and governance capabilities can provide organizations with a seamless and efficient way to ensure high-quality and consistent data streams to fuel real-time applications and use cases, while reducing operational burdens and costs."

« Le traitement de flux implique l'intégration et la transformation en temps réel de flux de données événementielles afin d'alimenter des systèmes dans tous les secteurs, allant de la détection de la fraude financière aux recommandations personnalisées dans l'e-commerce. Utilisés conjointement, Apache Flink et Apache Kafka permettent de réutiliser les données et d'éviter les traitements redondants en aval. Lorsqu'ils sont fournis en tant que services entièrement gérés, Flink et Kafka offrent des fonctionnalités de traitement de flux sans les difficultés habituellement liées à la gestion d'une infrastructure. Cela permet aux équipes de se concentrer sur la création de pipelines et d'applications de streaming en temps réel, et ainsi d'aider leur entreprise à se démarquer. »