Apache Spark for Administrators Training in Thimphu, Bhutan

This training course provides system administrators with the skills necessary to set up, configure, manage, and troubleshoot Apache Spark clusters. Participants will learn about Spark’s architecture, components, and how to optimize and scale Spark clusters in production environments. The course also covers best practices for monitoring, security, and troubleshooting common issues in Spark clusters.

Introduction to Apache Spark and Cluster Management

Module 1: Introduction to Apache Spark

Overview of Apache Spark and its ecosystem (RDDs, DataFrames, Datasets, etc.).

Spark’s architecture: Driver, Executors, and the Cluster Manager.

Understanding Spark’s processing model and job execution flow.

Module 2: Setting Up Spark Clusters

Installing Spark on a single node and multi-node clusters.

Configuring Spark with Hadoop (Standalone, YARN, Mesos).

Understanding Spark’s configuration properties and tuning them for optimal performance.

Managing Spark with cluster managers (Standalone, YARN, Mesos).

Module 3: Resource Management and Job Scheduling

Allocating resources with Spark (CPU, memory, and disk).

Managing Spark jobs and stages: task scheduling and execution.

Introduction to Spark’s job and stage monitoring tools.

Performance Tuning, Security, and Troubleshooting

Module 4: Spark Performance Tuning

Memory management and Spark’s internal memory model.

Optimizing Spark’s shuffle operations and reducing data skew.

Best practices for partitioning, caching, and persisting data.

Configuring Spark for large-scale data processing and minimizing job runtimes.

Module 5: Security Management in Spark

Setting up Spark security features (Kerberos authentication, SSL encryption, etc.).

Implementing data security and access control using Spark’s security models.

Understanding Spark’s role-based access control (RBAC) and integrating with external security systems.

Module 6: Troubleshooting and Debugging Spark Clusters

Diagnosing common Spark issues: slow jobs, resource bottlenecks, memory leaks.

Using Spark logs for troubleshooting job failures and performance degradation.

Tools and strategies for debugging Spark jobs (Spark UI, Spark History Server).

Managing cluster failures and automating recovery processes.

Hands-On Labs

60% of the training will involve practical exercises and case studies.