Run Apache Spark SQL workloads on Azure Cobalt 100 Arm64 using Gluten and Velox for accelerated analytics

content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_index.md

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+---
+title: Run Apache Spark SQL workloads on Azure Cobalt 100 Arm64 using Gluten and Velox for accelerated analytics
+
+draft: true
+cascade:
+    draft: true
+
+minutes_to_complete: 120
+
+who_is_this_for: This is an intermediate topic for data engineers, platform engineers, and developers who want to build and optimize high-performance Spark SQL workloads using native execution engines on Arm-based cloud environments.
+
+learning_objectives:
+  - Install and configure Hadoop, Spark, and Hive on Azure Cobalt 100 Arm64 virtual machines
+  - Build and integrate Gluten with the Velox backend for native query execution
+  - Configure Spark SQL for columnar and vectorized execution
+  - Generate and load TPC-DS datasets for benchmarking
+  - Run Spark SQL workloads and compare performance between vanilla Spark and Gluten + Velox
+
+prerequisites:
+  - A [Microsoft Azure account](https://azure.microsoft.com/) with access to Cobalt 100 based instances (Dpsv6)
+  - Basic knowledge of Linux command-line operations
+  - Familiarity with SSH and remote server access
+  - Basic understanding of distributed systems and Apache Spark
+
+author: Pareena Verma
+
+### Tags
+skilllevels: Introductory
+subjects: Performance and Architecture
+cloud_service_providers:
+  - Microsoft Azure
+
+armips:
+    - Neoverse
+
+tools_software_languages:
+  - Apache Spark
+  - Hadoop
+  - Hive
+  - Gluten
+  - Velox
+
+
+operatingsystems:
+    - Linux
+
+further_reading:
+  - resource:
+      title: Apache Spark Documentation
+      link: https://spark.apache.org/docs/latest/
+      type: documentation
+  - resource:
+      title: Gluten Project (Apache Incubator)
+      link: https://github.com/apache/incubator-gluten
+      type: documentation
+  - resource:
+      title: Velox Execution Engine
+      link: https://github.com/facebookincubator/velox
+      type: documentation
+  - resource:
+      title: TPC-DS Benchmark Overview
+      link: https://www.tpc.org/tpcds/
+      type: documentation
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1
+layout: "learningpathall"
+learning_path_main_page: "yes"
+---

content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/_next-steps.md

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+title: "Next Steps"         # Always the same, html page title.
+layout: "learningpathall"   # All files under learning paths have this same wrapper for Hugo processing.
+---

content/learning-paths/servers-and-cloud-computing/spark-velox-cobalt/background.md

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+---
+title: "Overview of Azure Cobalt 100 and Apache Spark with Gluten and Velox"
+
+weight: 2
+
+layout: "learningpathall"
+---
+
+## Azure Cobalt 100 Arm-based processor
+
+Azure’s Cobalt 100 is Microsoft’s first-generation, in-house Arm-based processor. Built on Arm Neoverse N2, Cobalt 100 is a 64-bit CPU that delivers strong performance and energy efficiency for cloud-native, scale-out Linux workloads such as web and application servers, data analytics, open-source databases, and caching systems. Running at 3.4 GHz, Cobalt 100 allocates a dedicated physical core for each vCPU, which helps ensure consistent and predictable performance.
+
+To learn more, see the Microsoft blog [Announcing the preview of new Azure VMs based on the Azure Cobalt 100 processor](https://techcommunity.microsoft.com/blog/azurecompute/announcing-the-preview-of-new-azure-vms-based-on-the-azure-cobalt-100-processor/4146353).
+
+## Apache Spark with Gluten and Velox
+
+Apache Spark is an open-source distributed data processing engine designed for large-scale data analytics. It provides high-level APIs for SQL, streaming, machine learning, and graph processing, and is widely used for building data pipelines and analytical workloads.
+
+By default, Spark executes queries using the JVM (Java Virtual Machine), which can introduce overhead in CPU-intensive workloads. To address this, modern acceleration frameworks like **Gluten** and **Velox** enable native execution for improved performance.
+
+**Gluten** is an open-source Spark plugin that offloads Spark SQL execution from the JVM to native engines. It acts as a bridge between Spark and high-performance backends, enabling efficient query execution while maintaining compatibility with existing Spark workloads.
+
+**Velox** is a high-performance, vectorized execution engine written in C++. It is optimized for modern hardware, including Arm64 architectures such as Azure Cobalt 100. Velox processes data in a columnar format and uses vectorized execution to significantly reduce CPU overhead and improve query performance.
+
+Together, **Gluten + Velox** provide:
+
+- Native (off-JVM) execution of Spark SQL queries  
+- Vectorized processing for faster computation  
+- Reduced memory and CPU overhead  
+- Improved performance on Arm-based infrastructure  
+
+To learn more, see:
+- [Apache Spark Documentation](https://spark.apache.org/docs/latest/)
+- [Gluten Project](https://github.com/apache/incubator-gluten)
+- [Velox Engine](https://github.com/facebookincubator/velox)
+
+
+### Key Capabilities
+
+- **Native Query Execution:**  
+  Spark SQL queries are executed using Velox instead of JVM-based execution.
+
+- **Columnar Processing:**  
+  Data is processed in columnar batches, improving cache efficiency and throughput.
+
+- **Vectorized Execution:**  
+  Multiple data values are processed in a single CPU instruction, accelerating computation.
+
+- **Hardware Optimization:**  
+  Velox is optimized for modern CPUs, including Arm64 (Azure Cobalt 100), delivering better performance per core.
+
+### In This Learning Path
+
+In this Learning Path, you will:
+
+- Deploy Apache Spark on an Azure Cobalt 100 Arm64 virtual machine  
+- Build and integrate Gluten with the Velox backend  
+- Configure Spark to use native execution  
+- Run Spark SQL workloads using Gluten + Velox  
+- Generate and load TPC-DS benchmark datasets  
+- Execute analytical queries and measure performance  
+- Compare accelerated workloads against vanilla Spark  
+