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.
