How can enterprises leverage real-time streaming applications?
Today, it is evident that data infrastructures are incredibly diverse. As a result, distributed data processing frameworks like Apache Flink need to be able to interact with several components including resource managers, filesystems, and services for distributed coordination.
What is Apache Flink?
Initiated by the founders of Ververica, formerly Data Artisans, the Apache Flink® open source framework addresses the inherent challenges of real time stream processing and analytics. Over the years, Apache Flink has become one of the fastest growing open source stream processing frameworks, providing measurable competitive advantages to their production users — including tech giants Alibaba, Netflix, Uber and Lyft, amongst others. Nevertheless, deploying Flink applications on-premise, in the cloud, or in hybrid environments can be challenging. In order to address these challenges, the latest book from Vasia Kalavri and Fabian Hueske, Stream Processing with Apache Flink discuss the fundamentals, implementation and operations of Streaming Applications with Apache Flink.
Becoming a real-time business
For companies looking for an enterprise ready solution, Ververica provides the Ververica Platform, based on open source Apache Flink and Ververica's Application Manager. The product delivers "accurate, low-latency, and fault-tolerant processing of large volumes of streaming data." With the Ververica Platform, enterprises can harness the power of stream processing in order to become a real-time business, able to build, deploy, and operate streaming applications based on Apache Flink in a secure, scalable, and cost-effective manner. Ververica is currently offering a complimentary chapter from the latest O'Reilly book “Stream Processing with Apache Flink” that covers the building blocks of getting Flink up and running for modern application development. The chapter demonstrates how companies can deploy Flink in a number of environments, including Standalone deployments, as well as on Docker, Yarn, and Kubernetes. The authors Kalavri and Hueske also explain Flink setups for different Hadoop versions and filesystems, alongside the most important configuration parameters of Flink's master and worker processes.
Looking to enhance your data strategy? Download the chapter in order to discover the fundamentals, implementation, and operation of streaming applications