Apache Spark for Developers

📅 Day 1: Understanding the Spark Ecosystem & Core Concepts

Learning Objectives:

• Gain a foundational understanding of Apache Spark architecture and execution models
• Learn how Spark fits into modern big data workflows and integrates with Hadoop and cloud environments
• Explore distributed computing concepts, including cluster management and parallel processing

Agenda:
✅ Introduction to Spark & Distributed Computing

• The role of Apache Spark in big data
• How Spark compares to traditional MapReduce
• Spark vs. Hadoop: Key differences and performance advantages

✅ The Spark Engine: How It Works

• Understanding SparkSession, Driver, Executors, and Cluster Managers
• Navigating DAG (Directed Acyclic Graphs) and the lazy evaluation model
• How Spark handles fault tolerance and job scheduling

✅ Working with Spark Data Structures

• Introduction to RDDs (Resilient Distributed Datasets), DataFrames, and Datasets
• Benefits and use cases for each data abstraction
• Loading, transforming, and persisting data for high-performance computation

✅ Hands-On Lab: Building Your First Spark Application

• Setting up Spark locally and in a cluster environment
• Writing your first Scala or Python Spark script
• Running basic transformations and actions on Spark DataFrames

📅 Day 2: Developing and Optimizing Spark Applications

Learning Objectives:

• Build scalable data processing pipelines with Spark
• Leverage Spark SQL and data transformations for analytics
• Implement streaming and real-time data processing
• Learn techniques to tune performance and optimize workflows

Agenda:
✅ Data Processing with Spark

• Understanding immutable transformations and actions
• Applying map, filter, groupBy, reduceByKey, and other key operations
• Managing data persistence and caching for efficiency

✅ Advanced Data Handling with Spark SQL

• Introduction to Spark SQL for structured data processing
• Writing SQL queries and integrating with Hive, JDBC, and NoSQL databases
• Registering temporary views and working with Spark Catalog

✅ Streaming Data Processing

• Introduction to real-time analytics and structured streaming
• Processing data streams from Kafka, Flume, and cloud sources
• Implementing window functions, event-time processing, and aggregations

✅ Performance Optimization & Debugging

• Understanding lazy execution and query optimization
• Efficiently partitioning and shuffling data for parallel execution
• Using broadcast variables and accumulators to enhance performance

✅ Hands-On Lab: Building Data Pipelines & Querying Large Datasets

• Writing SQL queries on Spark DataFrames
• Analyzing structured data with Spark SQL and aggregations
• Optimizing query execution plans using caching and indexing

📅 Day 3: Advanced Topics, Deployment & Real-World Use Cases

Learning Objectives:

• Gain expertise in deploying and monitoring Spark applications
• Learn best practices for fault tolerance and debugging
• Explore machine learning capabilities with MLlib
• Understand real-world Spark implementations and case studies

Agenda:
✅ Deploying Spark Applications

• Running Spark in local, standalone, YARN, and Mesos modes
• Submitting and managing applications using spark-submit
• Configuring resource allocation for efficiency

✅ Monitoring & Debugging Spark Jobs

• Using Spark UI and event logs for performance analysis
• Understanding stages, tasks, and execution graphs
• Debugging common Spark errors and performance bottlenecks

✅ Introduction to Machine Learning with MLlib

• Overview of MLlib’s machine learning algorithms
• Implementing classification, regression, and clustering models
• Using feature selection and dimensionality reduction techniques

✅ Real-World Case Studies & Best Practices

• Spark in finance, healthcare, IoT, and retail
• Handling large-scale datasets and enterprise Spark workflows
• Combining batch and streaming data for advanced analytics

✅ Hands-On Lab: Deploying a Spark Application & Running ML Algorithms

• Running a production-grade Spark application
• Deploying a machine learning model using Spark MLlib
• Using hyperparameter tuning for model optimization

Customization Options & Pricing

All courseware can be tailored to meet specific learning requirements:
✔ 5-day version available at $40/student per day
✔ Customizable into 4, 3, 2, or 1-day formats

This hands-on, high-impact training ensures learners gain real-world expertise in Apache Spark development, optimization, and deployment. 🚀

Instructor Kit

(PPTX/PDF of Slides + Optional Instructor Notes)
Comprehensive slide deck with detailed content covering all modules, plus optional instructor notes to enhance teaching effectiveness.

Student Kit / Handout

(with Free Branding)
Professionally designed handouts for students, including all essential course information and customizable branding options for your organization.

Course Agenda / Outline

Detailed day-by-day course agenda and outline, ensuring smooth course delivery and a structured learning experience for students.

Study Guide

A concise guide summarizing key concepts and topics covered in the course, perfect for post-course review and exam preparation.

FAQ

Answers to commonly asked questions about the course content, delivery, and labs to support instructors and students.

Briefing Doc

A high-level document summarizing the course objectives, target audience, and key learning outcomes, ideal for internal use and marketing.

Sales Enablement Kit for IT Training Sales Engineers

(Additional Fee)
Exclusive toolkit designed for IT training sales teams, including pitch decks, objection handling, and ROI documentation to support course sales.

Course AI GPT

(Course Assistant GPT so students can talk to the course materials!)
A cutting-edge AI-driven assistant that allows students to interact with course content, ask questions, and receive instant feedback.

Optional Podcast

(of the entire course or for each individual module)
Engaging audio content covering the entire course or individual modules, perfect for on-the-go learning or reinforcement.

Lab Guide

(Lab Environments are additional and can be found at CourseLabs.io)
Step-by-step lab guide to support hands-on learning, with lab environments available separately at CourseLabs.io.

Lab Files

(If you choose to host your own lab environment)
All necessary files and instructions for setting up and running labs in your own environment, offering flexibility in deployment.

Apache Spark – Version 2.1

Hadoop (HDFS, YARN) – Latest stable version

MapR Technologies (MapR-XD, MapR-DB) – Latest stable version

Apache Mesos – Latest stable version

Apache Hive – Latest stable version

HBase – Latest stable version

Scala – Latest stable version

Python – Latest stable version

Spark SQL – Version 2.1 (aligned with Apache Spark 2.1)

Spark Streaming – Version 2.1

MLlib (Machine Learning Library for Spark) – Version 2.1

GraphFrames for Apache Spark – Latest stable version

Structured Streaming – Version 2.1

Spark Interactive Shell – Supports Scala & Python

This Instructor-Led Training (ILT) course is designed to equip developers, data engineers, and big data professionals with the skills needed to build, optimize, and deploy Apache Spark applications. The course is structured as 50% lecture and 50% hands-on labs, ensuring a balanced learning experience that combines theoretical knowledge with real-world application.

Course Objectives

By the end of this course, learners will:
✅ Understand Apache Spark architecture, execution models, and cluster management
✅ Develop scalable, high-performance Spark applications using Datasets, DataFrames, and RDDs
✅ Utilize Spark SQL, Structured Streaming, and MLlib for data analysis, real-time processing, and machine learning
✅ Optimize performance through caching, partitioning, and tuning techniques
✅ Integrate Spark with Hadoop, YARN, HDFS, HBase, and cloud platforms
✅ Build and monitor Spark applications with best practices for fault tolerance and deployment

Who Should Take This Course?

This course is ideal for:

• Software Developers & Engineers looking to develop big data applications
• Data Engineers & Architects needing hands-on experience with Spark-based pipelines
• Data Scientists & Analysts working with large-scale data processing & machine learning
• System Administrators supporting Spark clusters & enterprise data environments

Flexible Course Delivery Options

All courseware is fully customizable to fit different training needs. This course can be delivered as:
✔ 5-day training at $40 per student, per day
✔ Condensed into 4, 3, 2, or even 1-day formats to accommodate different learning requirements

With a focus on real-world application and enterprise readiness, this course provides an invaluable learning experience for professionals looking to master Apache Spark and advance their big data skills.

📢 Customize your training today and offer high-impact Spark education to your clients! 🚀