Data Engineering with Apache Spark

Day 1: Introduction to Spark & Big Data Processing

Learning Objectives:

• Understand Spark’s role in modern big data ecosystems
• Identify core Spark components and their functions
• Explore different execution environments for Spark applications
• Learn how Spark processes large-scale datasets
• Set up a Spark development environment

Agenda:

✅ Overview of big data challenges & Spark’s advantages
✅ Comparing MapReduce and Spark: Speed, Efficiency & Use Cases
✅ Understanding Spark’s core architecture: Drivers, Executors & Cluster Managers
✅ Exploring Spark’s key components: RDDs, DataFrames, Spark SQL, and Streaming
✅ Hands-on Lab: Launching the Spark shell and running basic queries

Day 2: Working with Data – Transformations & Optimizations

Learning Objectives:

• Load and manipulate structured and unstructured datasets
• Perform data transformations using RDDs and DataFrames
• Optimize data processing with caching and partitioning
• Understand lazy evaluation and DAG execution in Spark
• Troubleshoot common performance bottlenecks

Agenda:

✅ Loading data from local files, HDFS, and cloud storage
✅ Working with Resilient Distributed Datasets (RDDs) and DataFrames
✅ Applying filtering, mapping, joins, and aggregation operations
✅ Optimizing Spark performance through partitioning & caching techniques
✅ Hands-on Lab: Processing structured and unstructured data using DataFrames

Day 3: Building & Deploying Scalable Spark Applications

Learning Objectives:

• Develop and execute standalone Spark applications
• Understand cluster resource management strategies
• Deploy applications using YARN, Mesos, and Kubernetes
• Implement error handling and logging for Spark jobs
• Debug and optimize long-running Spark applications

Agenda:

✅ Setting up a Spark project using Scala or Python
✅ Writing and running batch jobs and real-time processing tasks
✅ Managing resources using YARN, Mesos & Kubernetes
✅ Handling failures, logging, and debugging Spark applications
✅ Hands-on Lab: Deploying and monitoring a Spark application on a cluster

Day 4: Advanced Analytics & Real-Time Processing in Spark

Learning Objectives:

• Integrate machine learning algorithms with Spark MLlib
• Perform real-time data streaming and event processing
• Work with graph analytics using GraphX
• Implement advanced query optimization techniques
• Apply Spark best practices for enterprise-scale projects

Agenda:

✅ Exploring Spark Streaming for real-time analytics
✅ Using MLlib for predictive modeling and clustering
✅ Introduction to GraphX for graph processing and social network analysis
✅ Query tuning and advanced optimization techniques
✅ Hands-on Lab: Building a real-time analytics pipeline with Spark Streaming

Final Project & Wrap-Up:

✅ Capstone project: Building an end-to-end Spark application
✅ Q&A session and best practices for real-world implementation
✅ Discussion on customizing Spark training for enterprise needs

📩 Want to customize this training for your team? Contact us today!

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 1.4.1 (mentioned)

Scala – Latest Stable Version

Python – Latest Stable Version (for Spark Interactive Shell)

SparkR – Latest Stable Version (Introduced in Spark 1.4.1)

Hadoop – Latest Stable Version (Compatible with Spark)

YARN – Latest Stable Version (Used for resource management)

Apache Mesos – Latest Stable Version

HDFS – Latest Stable Version

MapR-FS – Latest Stable Version

HBase – Latest Stable Version

Hive – Latest Stable Version

JDBC Databases – Latest Stable Version (For external data integration)

MapR Sandbox – Required for on-demand course (Version not specified)

Spark SQL – Latest Stable Version

Spark Streaming – Latest Stable Version

MLlib (Machine Learning Library) – Latest Stable Version

GraphX – Latest Stable Version

This 4-day instructor-led training on Developing Spark Applications is designed to provide a 50% lecture and 50% hands-on lab experience, ensuring participants gain both theoretical knowledge and practical expertise. Whether your clients need a full deep dive or a condensed version, this course can be customized into 1, 2, 3, 4, or 5-day formats to suit their specific training needs. Pricing starts at $40 per student per day.

Course Objectives

By the end of this course, participants will be able to:
✔️ Understand the core architecture of Apache Spark and its components
✔️ Develop and execute standalone Spark applications
✔️ Work with RDDs, DataFrames, and Spark SQL for data transformation
✔️ Integrate Spark with real-time streaming and machine learning models
✔️ Optimize performance through caching, partitioning, and tuning
✔️ Deploy Spark applications across YARN, Mesos, and Kubernetes

Who Should Take This Course?

This course is designed for:
✔️ Data Engineers who need to process large-scale datasets efficiently
✔️ Software Developers looking to build high-performance Spark applications
✔️ Data Scientists seeking to integrate machine learning with Spark
✔️ Big Data Architects planning enterprise Spark deployments
✔️ IT Professionals & Administrators managing Spark environments

Hands-On Learning Approach

This course is structured as 50% lecture and 50% hands-on labs, ensuring participants gain practical experience with real-world data processing scenarios. Labs include:
✅ Loading and transforming data with RDDs and DataFrames
✅ Building and optimizing Spark applications
✅ Real-time data streaming exercises
✅ Performance tuning and debugging techniques

Flexible Course Format & Pricing

We understand that training needs vary, which is why this course can be customized into different durations:
🗓️ 1, 2, 3, 4, or 5-day formats available
💲 $40 per student per day

📩 Contact us today to customize this course for your training audience!