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Confluent Announces General Availability of Confluent Cloud for Apache Flink

Confluent

Confluent Cloud for Apache Flink, a leading cloud-native, serverless Flink service is now available on AWS, Google Cloud, and Microsoft Azure

Confluent announced the general availability of Confluent Cloud for Apache Flink, a fully managed service for Apache Flink that enables customers to process data in real time and create high-quality, reusable data streams. Confluent Cloud for Apache Flink is available across Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. Backed by Confluent’s 99.99% uptime SLA, Confluent’s cloud-native service for Flink enables reliable, serverless stream processing.

“Flink’s high performance, low latency, and strong community make it the best choice for developers to use for stream processing.”

Shaun Clowes, Chief Product Officer, Confluent.

As the de facto stream processing standard, Flink is relied upon by innovative companies like Airbnb, Uber, Netflix, and Stripe to support mission-critical streaming workloads. As a leading cloud-native, serverless Flink offering, Confluent Cloud for Apache Flink enables customers to easily build high-quality, reusable data streams to power all of their real-time applications and analytics needs.

“Stream processing allows organizations to transform raw streams of data into powerful insights,” said Shaun Clowes, chief product officer at Confluent. “Flink’s high performance, low latency, and strong community make it the best choice for developers to use for stream processing. With Kafka and Flink fully integrated in a unified platform, Confluent removes the technical barriers and provides the necessary tools so organizations can focus on innovating instead of infrastructure management.”

Apache Flink powers real-time use cases and next-generation experiences

Flink powers the real-time use cases customers demand. Flink enables customers to build streaming data pipelines, event-driven applications, and real-time analytics to power use cases like personalized recommendations, dynamic pricing, and anomaly detection. Confluent Cloud for Apache Flink® is an easy way for companies to get started with these stream processing use cases.

Easier AI development with streaming data pipelines

Confluent Cloud for Apache Flink enables users to build streaming data pipelines for vector databases with ease, ensuring generative AI applications have access to their organization’s most relevant and valuable data in real time. Confluent integrates with leading vector database vendors, such as Elastic, Pinecone, Rockset, SingleStore, and Zilliz to further simplify and accelerate the development of critical generative AI initiatives even further.

More accurate real-time alerts for event-driven applications

With Confluent Cloud for Apache Flink, customers can build next-gen event-driven applications that can analyze transaction data and trigger alerts for faster fraud detection or help stores better manage their inventory and share accurate delivery times.

Faster decisions for real-time analytics

Flink can process massive amounts of data quickly with low, sub-second latency, interactive queries, and advanced pattern recognition functions. Confluent Cloud for Apache Flink® can manage and process billions of data points for timely movie, show, and music recommendations and provide up-to-date information on order volumes, popular menu items, and delivery times for food delivery.

Confluent Cloud for Apache Flink® is now generally available on all three major cloud service providers. Customers can sign up for Confluent Cloud to easily access Confluent’s fully managed offering for Flink. To help customers quickly build and deploy Flink applications, Confluent has a global network of system integrators, including Deloitte, Ness Digital Engineering, Somerford Associates, Improving, Psyncopate, Platformatory, Synthesis Software Technologies (Pty) Ltd and iLink Digital. These system integrators can provide on-site engineering assistance and technical support to help customers accelerate their stream processing use cases.

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