Administration of Apache Spark

• Basic knowledge of network configuration and management
• Familiarity with Linux operating system and command-line interface
• Interest in learning about distributed computing systems and big data management

Audience

• System administrators

Apache Spark is an open-source, unified analytics engine for large-scale data processing.

This instructor-led, live training (online or onsite) is aimed at beginner-level to intermediate-level system administrators who wish to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

Introduction to Apache Spark

• The role of Spark in big data processing
• Spark architecture and its components

Setting Up Apache Spark

• Hardware and software requirements
• Installation procedures for standalone and cluster modes
• Configuration best practices for system administrators

Administering Spark Clusters

• Cluster management tools and techniques
• Monitoring Spark applications and cluster resources
• Security configurations and user management

Performance Tuning and Optimization

• Resource allocation and scheduling
• Tuning Spark for optimal performance
• Identifying and resolving common bottlenecks

Troubleshooting and Problem-Solving

• Common Spark administration challenges
• Diagnostic tools and techniques for troubleshooting
• Step-by-step approach to resolving common issues
• Best practices for maintaining a healthy Spark environment

Advanced Administration Topics

• Integration with other big data tools
• Ensuring high availability and disaster recovery
• Upgrading and scaling Spark clusters

Summary and Next Steps