Apache Beam on Ubuntu26 is a pre-configured virtual machine image designed for developers, data engineers, and organizations that require a fast, reliable, and scalable environment for building and executing data processing pipelines on Microsoft Azure. This image provides Apache Beam pre-installed on Ubuntu 26 along with Python and all required dependencies, enabling users to start developing and running pipelines immediately without the complexity of manual software installation and environment configuration.

Apache Beam is an open-source unified programming model that simplifies the development of both batch and streaming data processing applications. It provides a portable API that allows developers to write data pipelines once and execute them across multiple distributed processing engines. Apache Beam supports a wide range of runners, including Apache Spark, Apache Flink, Google Cloud Dataflow, and Direct Runner for local development and testing.

The platform is optimized for large-scale data processing workloads and offers a flexible architecture for handling structured and unstructured datasets. Developers can leverage Beam’s rich set of transforms and SDKs to build ETL pipelines, stream analytics applications, event-driven architectures, and machine learning data preparation workflows. The included Python SDK and supporting libraries provide a ready-to-use development environment for creating and deploying modern data engineering solutions.

This Azure Marketplace image is suitable for real-time data processing, batch ETL pipelines, stream analytics, event-driven data processing, data engineering projects, analytics workloads, and learning or experimentation with Apache Beam. Organizations can accelerate project delivery by deploying a pre-configured environment that eliminates installation overhead and ensures compatibility with widely used data processing frameworks.

Key Features:

• Apache Beam pre-installed and configured
• Ubuntu 26 LTS operating system
• Python SDK with required libraries
• Support for batch and streaming data pipelines
• Ready for local development and testing
• Compatible with runners such as Apache Spark, Apache Flink, and Google Cloud Dataflow
• Easy deployment on Microsoft Azure Marketplace

Use Cases:

• Real-time data processing
• Batch ETL pipelines
• Stream analytics
• Event-driven data processing
• Data engineering and analytics projects
• Learning and experimentation with Apache Beam

To subscribe to this product from Azure Marketplace and deploy an instance using Azure Compute services, follow the steps below:

1. Navigate to Azure Marketplace and subscribe to the required product.
2. Search for Virtual Machines under Azure Services.
3. Click Add to open the Create a Virtual Machine page.

Create Virtual Machine

4. Under the Basics tab:
   
   • Select the appropriate subscription.
   • Create a new resource group (for example: myResourceGroup).
5. Under Instance Details:
   
   • Virtual Machine Name: myVM
   • Region: East US
   • Image: Ubuntu Server 24.04 LTS x64 Gen1/2
   • Keep remaining settings as default unless customization is required.
6. Under Administrator Account:
   
   • Authentication type: Password
   • Enter a username.
   • Create and confirm a strong password (use a secure combination of letters, numbers, and special characters).
7. Under Inbound Port Rules:
   
   • Select Allow selected ports
   • Enable:
     
     • SSH (22)
     • HTTP (80)
     • HTTPS (443)
8. Review the configuration and click Review + Create, then select Create to start deployment.

Deployment will take a few minutes to complete.

Connect to the Virtual Machine

1. Open the VM Overview page and click Connect.
2. Copy the public IP address of the virtual machine.
3. Open a terminal (Linux/macOS) or use PuTTY/Windows Terminal.
4. Connect using the following command:

ssh username@public-ip-address

5. When prompted, enter the password you created during VM setup to access the virtual machine.

Usage/Deployment Instructions

The complete deployment guide for this product is available in a detailed PDF document.
Please refer to the official deployment manual for step-by-step instructions, configuration details, and troubleshooting guidance.

Deployment Guide: Click Here