Apache Spark 

Course Description:

This course provides a comprehensive introduction to Apache Spark, a powerful framework for large-scale data processing. Spark offers significant performance improvements over traditional MapReduce approaches. You'll gain hands-on experience writing both batch processing and streaming applications using Spark.

Target Audience:

Developers tasked with building Spark applications. Prior experience with Scala or Python is recommended.

Course Objectives:

• Understand the core concepts and functionalities of Apache Spark.

• Utilize Spark APIs for interactive analysis and large-scale data processing.

• Compare and contrast Spark with other big data processing frameworks.

Course Length: 3 Days

Course Outline:

Module 1: Introduction to Apache Spark

• Introduction: What is Apache Spark?

• Getting Started: Using the Spark Shell

• Resilient Distributed Datasets (RDDs): The foundation of Spark

• Functional Programming with Spark: Understanding the programming paradigm

• The Hadoop Distributed File System (HDFS): Understanding data storage for Spark

• Sub-module (Optional): HDFS Deep Dive (Why HDFS, Architecture, Usage with Spark)

• Spark and the Big Data Ecosystem: Spark's role in the broader landscape

• Spark vs. MapReduce: Understanding the differences and advantages of Spark

Module 2: Working with RDDs

• RDDs Explained: Structure and operations for distributed data processing

• Key-Value Pair RDDs: A fundamental data structure

• MapReduce and Pair RDD Operations: Leveraging familiar concepts for distributed processing

• Running Spark on a Cluster: Setting up and managing Spark clusters

  • Standalone Cluster: A simple deployment option

  • Spark Standalone Web UI: Monitoring and managing your cluster

  • Using the Databricks environment

Module 3: Parallel Programming with Spark

• RDD Partitions and HDFS Data Locality: Optimizing performance through data placement

• Working with Partitions: Efficient data processing through parallel operations

• Caching and Persistence: Strategies for efficient data access

• Distributed Persistence and Caching Mechanisms

Module 4: Building Spark Applications

• SparkContext: The starting point for Spark applications

• Spark Properties: Configuring Spark applications

• Building and Running Spark Applications: Putting theory into practice

• Logging: Capturing and managing application logs

Module 5: Spark Streaming

• Introduction to Spark Streaming: Processing real-time data streams

• Sliding Window Operations: Analyzing data streams over defined windows

• Building Spark Streaming Applications: Creating streaming applications

Module 6: Advanced Spark Topics 

1. Common Spark Algorithms: Exploring built-in algorithms for iterative tasks, graph analysis, and machine learning

2. Performance Optimization: Techniques for improving Spark application performance

   • Shared Variables: Broadcast Variables and Accumulators for efficient data sharing

   • Identifying and addressing common performance bottlenecks

Note: This class leverages the Databricks environment. If your company uses another Spark environment, the workshop can be adapted.