# Get started with Apache Flink on Google Axion C4A

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/)
- [Get started with Apache Flink on Google Axion C4A](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/background/)
- [Create a Google Axion C4A Arm virtual machine](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/instance/)
- [Install Apache Flink](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/installation/)
- [Test Flink baseline functionality](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/baseline/)
- [Benchmark Flink performance](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/benchmarking/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/flink-on-gcp/_next-steps/)

## Explore Google Axion C4A Arm instances
Google Axion C4A is a family of Arm-based virtual machines built on Google’s custom Axion CPU, which is based on Arm Neoverse-V2 cores. Designed for high-performance and energy-efficient computing, these virtual machines offer strong performance for modern cloud workloads such as CI/CD pipelines, microservices, media processing, and general-purpose applications.

The C4A series provides a cost-effective alternative to x86 virtual machines while leveraging the scalability and performance benefits of Arm architecture in Google Cloud.

To learn more about Google Axion, see the Google blog [Introducing Google Axion Processors, our new Arm-based CPUs](https://cloud.google.com/blog/products/compute/introducing-googles-new-arm-based-cpu).

## Explore Apache Flink
[Apache Flink](https://flink.apache.org/) is an open-source, distributed stream and batch data processing framework developed under the [Apache Software Foundation](https://www.apache.org/).

Flink is designed for high-performance, low-latency, and stateful computations on both unbounded (streaming) and bounded (batch) data. It provides a robust runtime and APIs in Java, Scala, and Python for building scalable, fault-tolerant data processing pipelines.

Flink is widely used for real-time analytics, event-driven applications, data pipelines, and machine learning workloads. It integrates seamlessly with popular systems such as Apache Kafka, Hadoop, and various cloud storage services.

To learn more, visit the [Apache Flink official website](https://flink.apache.org/) and explore the [documentation](https://nightlies.apache.org/flink/flink-docs-release-2.1/).