Table of contents

1: Introduction to Big Data and Apache Spark
1.1 Overview of Big Data
1.2 Introduction to Apache Spark
1.3 History and Evolution of Spark
 1.4 Spark Ecosystem and Components
1.4.1 Spark Core
1.4.2 Spark SQL
1.4.3 Spark Streaming
1.4.4 MLlib
1.4.5 GraphX
  1.5 Installing and Setting Up Spark
1.5.1 Standalone Cluster Mode
1.5.2 Local Mode
1.5.3 Cluster Managers
1.5.3.1 YARN
1.5.3.2 Mesos
1.5.3.3 Kubernetes

2: Java Programming Fundamentals
2.1 Java Basics
2.2 Java Syntax and Data Types
  2.3 Control Structures
2.3.1 Conditionals
2.3.2 Loops
  2.4 Object-Oriented Programming in Java
2.4.1 Classes and Objects
2.4.2 Inheritance
2.4.3 Polymorphism
2.4.4 Encapsulation
2.4.5 Abstraction
2.5 Exception Handling in Java
  2.6 Java Collections Framework
2.6.1 Lists
2.6.2 Sets
2.6.3 Maps
2.6.4 Iterators
2.6.5 Streams

3: Working with Spark and Java
  3.1 Setting Up Java Development Environment for Spark
3.1.1 Installing JDK
3.1.2 IDE (IntelliJ IDEA, Eclipse)
3.1.3 Maven and SBT for Dependency Management
3.2 Spark Core Concepts
3.2.1 RDDs (Resilient Distributed Datasets)
3.2.2 Creating RDDs
3.2.3 Transformations
3.2.4 Actions
3.2.5 Pair RDDs

4: Spark SQL and DataFrames
4.1 Introduction to Spark SQL
4.2 SQLContext and HiveContext
4.3 DataFrames API
4.3.1 Creating DataFrames from Various Data Sources
4.3.1.1 CSV
4.3.1.2 JSON
4.3.1.3 Parquet
    4.3.2 DataFrame Operations
4.3.2.1 Filtering
4.3.2.2 Aggregation
4.3.2.3 Joins
4.4 DataFrame vs. SQL Queries
4.5 Working with Datasets

5: Spark Streaming
5.1 Introduction to Spark Streaming
5.2 DStreams (Discretized Streams)
5.3 Transformations on DStreams
5.4 Integrating with Other Sources
5.4.1 Kafka(Ref: Apache Storm with Kafka & Messaging Systems)
5.4.2 Flume
5.5 Stateful Operations
5.6 Windowed Operations

6: Machine Learning with MLlib
6.1 Introduction to Machine Learning and MLlib
6.2 MLlib Overview
  6.3 Data Types in MLlib
6.3.1 Vectors
6.3.2 Labeled Points
  6.4 Basic ML Algorithms
    6.4.1 Classification
6.4.1.1 Logistic Regression
6.4.1.2 Decision Trees
    6.4.2 Regression
6.4.2.1 Linear Regression
   6.4.3 Clustering
6.4.3.1 K-Means
6.5 Building and Evaluating Machine Learning Models

7: Advanced Topics in Spark
7.1 Performance Tuning
7.2 Memory Management and Optimization
7.3 Caching and Persistence
7.4 Serialization
  7.5 Spark GraphX
7.5.1 Introduction to Graph Processing
7.5.2 Basic Graph Operations
    7.5.3 Integration with Hadoop Ecosystem
7.5.3.1 HDFS
7.5.3.2 HBase
7.5.3.3 Other Data Sources

8: Project Work
8.1 Building a Big Data Application with Java and Spark
  8.2 End-to-End Project
8.2.1 Data Ingestion
8.2.2 Processing
8.2.3 Analysis
8.3 Best Practices and Optimization Techniques

9: Deployment and Monitoring
9.1 Deploying Spark Applications
9.2 Packaging and Submitting Applications
9.3 Running on Different Cluster Managers
9.4 Monitoring and Logging
9.5 Using Spark UI
 9.6 Integrating with Monitoring Tools
9.6.1 Ganglia
9.6.2 Graphite

10: Case Studies and Real-World Applications
10.1 Case Studies on Real-World Big Data Applications
10.2 Industry Use Cases of Apache Spark