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Confluent, Inc.’s release of its Stream Designer enables developers to build and deploy streaming data pipelines in minutes.
“We are in the middle of a major technological shift, where data streaming is making real time the new normal, enabling new business models, better customer experiences, and more efficient operations,” said Jay Kreps, Cofounder and CEO, Confluent. “With Stream Designer we want to democratize this movement towards data streaming and make real time the default for all data flow in an organisation.”
“A rising number of organisations are realising streaming data is imperative to achieving innovation and maintaining a healthy business,” said Amy Machado, Research Manager, Streaming Data Pipeline, IDC. “Businesses need to add more streaming use cases, but the lack of developer talent and increasing technical debt stand in the way. Visual interfaces, like Stream Designer, are key advancements to overcoming these challenges and make it easier to develop data pipelines for existing teams and the next generation of developers.”
“Data streaming is quickly becoming the central nervous system of our infrastructure as it powers real-time customer experiences across our 12 countries of operations,” said Enes Hoxha, Enterprise Architect, Raiffeisen Bank International. “Stream Designer’s low-code, visual interface will enable more developers, across our entire organisation, to leverage data in motion. With a unified, end-to-end view of our streaming data pipelines, it will improve our developer productivity by making real-time applications, pipeline development, and troubleshooting much easier.”
Stream Designer provides developers a flexible point-and-click canvas to build pipelines in minutes and describe data flows and business logic easily within the Confluent Cloud UI. It takes a developer-centric approach, where users with different skills and needs can seamlessly switch between the UI, a code editor, and command-line interface to declaratively build data flow logic at top speed. It brings developer-oriented practices to pipelines, making it easier for developers new to Kafka to scale data streaming projects faster.
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