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Course Outline
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
Requirements
- 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
35 Hours
Testimonials (5)
A lot of practical examples, different ways to approach the same problem, and sometimes not so obvious tricks how to improve the current solution
Rafał - Nordea
Course - Apache Spark MLlib
very interactive...
Richard Langford
Course - SMACK Stack for Data Science
Sufficient hands on, trainer is knowledgable
Chris Tan
Course - A Practical Introduction to Stream Processing
Get to learn spark streaming , databricks and aws redshift
Lim Meng Tee - Jobstreet.com Shared Services Sdn. Bhd.
Course - Apache Spark in the Cloud
practice tasks