Apache Spark

Apache Spark 4.0.1 is a high-performance, open-source distributed data processing and analytics framework designed for large-scale data engineering, machine learning, streaming, and real-time analytics workloads.

The solution supports common big data and analytics workflows including distributed data processing, SQL analytics, machine learning, ETL pipelines, graph processing, and streaming analytics. Apache Spark integrates with Hadoop, Hive, Kafka, Parquet, and cloud platforms, making it ideal for developers, data engineers, data scientists, and enterprise analytics teams working with large-scale data processing environments on Azure.

Version: Apache Spark 4.0.1

Features of Apache Spark:

• Fast distributed in-memory data processing engine.
• Support for batch processing and real-time stream analytics.
• Built-in Spark SQL for structured data processing.
• Machine learning support through MLlib.
• Graph processing support using GraphX.
• Integration with Hadoop, Hive, Kafka, Parquet, and cloud storage.
• Supports Python, Scala, Java, and R programming languages.
• Web-based monitoring dashboard for Spark applications and clusters.

Usage instructions for Apache Spark
$ sudo su
$ cd /opt
$ spark-submit --version

Testing Apache Spark installation

Access information:
Apache Spark provides a web-based monitoring dashboard.

Default Spark Web UI:
http://SERVER-IP:4040

Required Azure inbound ports:
SSH Port: 22
Spark Web UI Port: 4040

Disclaimer: Apache Spark is provided “as is” under applicable open-source licenses. Users are responsible for proper installation, cluster configuration, workload optimization, data validation, and secure management of distributed analytics environments. This solution is best suited for big data processing, ETL workflows, machine learning, streaming analytics, and enterprise-scale distributed computing workloads in development and production environments.