Data streaming pioneer, Confluent Inc makes the general availability of Confluent Cloud across Amazon Web Services (AWS), Google Cloud, and Microsoft Azure.

Supported by Confluent’s 99.9 percent SLA, the Confluent Cloud for Apache Flink®, promises complete managed service for Apache Flink®, enabling customers to process data in real time and create high-quality, reusable data streams.

Additionally, the cloud-native service for Flink enables a reliable and serverless streaming process. It supports companies like Airbnb, Uber, Netflix, and Stripe regarding mission-critical streaming workloads.

"Stream processing is essential for extracting timely insights from continuous data streams to power a wide range of critical use cases including fraud detection, dynamic pricing, and real-time inventory and supply chain management,” said Stewart Bond, research VP, data integration and data intelligence software at IDC.

“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”, added Bond.

 

Customers can effortlessly build high-quality, reusable data streams to power their real-time applications and analytics needs with the Confluent Cloud for Apache Flink®.

Shaun Clowes, chief product officer at Confluent says, “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.”

"Conditions in the automotive logistics industry can change rapidly, requiring immediate action to address delays, reroute vehicles, and update systems and customers," said Jeffrey Jennings, sr. consultant, data and integration services at ACERTUS.

“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”, said Sami AlAshabi, solutions architect at Essent.

Apache Flink Powers Real-Time Use Cases and Next-Generation Experiences

Through Flink, customers can build streaming data pipelines, event-driven applications, and real-time analytics for use cases like personalized recommendations, dynamic pricing, and anomaly detection.

Easier AI Development with Streaming Data Pipelines

Flink can help create streaming data pipelines that can ensure vector databases are supplied with cleaned, business-contextualized, real-time data to support generative AI applications.

More Accurate Real-Time Alerts for Event-Driven Applications

Through Flink, data streams can be analyzed and alerts can be sent during any specific event or pattern that may take place within the event-driven applications.

Faster Decisions for Real-Time Analytics

Flink can aid business in quick decision-making by using the platform to analyze real-time data streams and generate insights.