Deploy High-Performance Analytics with Apache Arrow and Arrow Flight on Google Cloud C4A Axion processors
Introduction
Get started with Apache Arrow and Arrow Flight on Google Axion C4A
Create firewall rules on GCP for Apache Arrow, MinIO, and Arrow Flight
Create a Google Axion C4A arm64 virtual machine on GCP
Set up Apache Arrow and MinIO on arm64
Analyze columnar data with Apache Arrow on arm64
Run high-speed analytics with Apache Arrow Flight on arm64
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  30 min
Last updated:  29 Jun 2026
Skill level:
  Introductory
Reading time:
  30 min
Last updated:
  29 Jun 2026
Author:Pareena Verma, Arm
Arm IP:Neoverse
Tags: Performance and Architecture Google Cloud Linux Apache Arrow Arrow Flight Python MinIO
Author:
  • Pareena Verma, Arm
Arm IP:
Neoverse
Tags:
Performance and Architecture Google Cloud Linux Apache Arrow Arrow Flight Python MinIO

Who is this for?

This is an introductory topic for data engineers, platform engineers, and developers who aim to build high-performance analytics pipelines on Arm64-based Google Cloud C4A Axion processors using Apache Arrow and Arrow Flight.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Deploy Apache Arrow–based data processing workloads on Google Cloud C4A Axion processors
  • Set up and run an Arrow Flight server for high-throughput, low-latency data transport
  • Read and write columnar data formats such as Parquet and ORC using Apache Arrow
  • Integrate Arrow with object storage (MinIO) for cloud-native analytics workflows
  • Validate performance benefits of Arrow and Arrow Flight on Arm-based infrastructure

Prerequisites

Before starting, you will need the following:

  • A Google Cloud Platform (GCP) account with billing enabled
  • Basic familiarity with Python
  • Basic understanding of data formats such as Parquet or ORC
  • Familiarity with Linux command-line operations

Summary

AI-assisted

This summary was drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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You’ll set up a single-node analytics stack on Google Cloud C4A virtual machines powered by Google Axion processors. First, you’ll provision an arm64 C4A instance, configure Google Cloud firewall rules for MinIO and Arrow Flight, and prepare a SUSE Linux Enterprise Server environment with Apache Arrow. Then, you’ll integrate MinIO object storage and use Apache Arrow to read and write Parquet and ORC datasets, and explore predicate pushdown and column pruning in a practical workflow. By the end, you’ll have a working setup that serves and moves columnar data with Arrow Flight and validates end-to-end data access using Arrow with MinIO on Arm-based infrastructure.

Frequently asked questions

AI-assisted

These FAQs were drafted with an approved AI-assisted workflow and reviewed by Arm contributors before publication. Human technical review remains part of the process so the final page reflects engineering rigor, accuracy, and Arm editorial standards.

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Which Google Cloud instance and OS image should I choose for this setup?
Use a Google Axion C4A arm64 virtual machine; the steps use c4a-standard-4 (4 vCPUs, 16 GB). Select an arm64 SUSE Linux Enterprise Server image as shown in the Learning Path.
Which network ports must be opened for the stack to work?
Open the ports listed in the firewall step, including MinIO’s S3 API on port 9000 and the Arrow Flight port specified there. After creating the rule, confirm that inbound connections to those services succeed from allowed sources.
How do I confirm MinIO is ready before running Arrow jobs?
Verify the MinIO service is running and accessible on the configured port (including 9000 for the S3 API). Follow the Learning Path instructions to store a test object and read it back to validate access.
What result should I expect when testing predicate pushdown and column pruning with Arrow?
Operations should return only the filtered rows and the selected columns from Parquet or ORC. If unfiltered rows or extra columns appear, review the dataset, filter, and projection arguments used in the example.
How do I know the Arrow Flight server is reachable from a client?
Use the address and port from the steps. The client should connect and list or fetch data without errors. Connection timeouts or refusals usually indicate a missing firewall rule or a server that’s not running.
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