1: Introduction to Big Data and Apache Spark
1.1 Overview of Big Data and Its Challenges
1.2 Introduction to Apache Spark and Its Ecosystem
1.3 Spark’s Architecture: Master, Worker Nodes, and Cluster Manager
1.4 Spark Components: Spark Core, Spark SQL, Spark Streaming, MLlib, GraphX

2: Setting Up Apache Spark and Java Development Environment
2.1 Installing and Configuring Apache Spark
2.2 Setting Up a Java Development Environment for Spark
2.3 Working with Spark’s APIs in Java
2.4 Running Spark Applications Locally and on a Cluster

3: Core Concepts of Apache Spark
3.1 Understanding RDDs (Resilient Distributed Datasets)
3.2 Transformations and Actions on RDDs
3.3 DataFrames and Datasets: Differences and Use Cases
3.4 Spark’s Catalyst Optimizer and Tungsten Execution Engine

4: Data Processing with Apache Spark
4.1 Loading and Saving Data from Various Sources (HDFS, S3, JDBC)
4.2 Data Cleaning and Transformation Techniques
4.3 Aggregations, Filtering, and Sorting Data
4.4 Using SparkSQL for Structured Data Queries

5: Advanced Data Processing Techniques
5.1 Handling Complex Data Types (Arrays, Maps, Structs)
5.2 Working with Semi-Structured and Unstructured Data (JSON, Avro)
5.3 Optimization Strategies for Large-Scale Data Processing
5.4 Partitioning and Shuffling Data for Performance Improvements

6: Data Analysis and Visualization
6.1 Performing Exploratory Data Analysis with Spark(Ref: Real-Time Data Processing with Java and Apache Spark Streaming)
6.2 Generating Summary Statistics and Data Distributions
6.3 Visualizing Data with Spark and Integration with Visualization Tools
6.4 Creating Interactive Dashboards and Reports

7: Machine Learning with Spark MLlib
7.1 Introduction to Spark MLlib and Machine Learning Pipelines
7.2 Building and Training Machine Learning Models in Spark
7.3 Evaluating and Tuning Machine Learning Models
7.4 Real-World Examples: Classification, Regression, Clustering

8: Advanced Analytics with Spark
8.1 Performing Graph Analytics with Spark GraphX
8.2 Real-Time Data Processing and Analytics with Spark Streaming
8.3 Combining Batch and Real-Time Data Processing in a Single Pipeline
8.4 Implementing Advanced Analytics Solutions Using Spark

9: Performance Tuning and Optimization
9.1 Understanding Spark’s Performance Metrics and Bottlenecks
9.2 Configuring Spark for Optimal Performance
9.3 Optimizing Spark Jobs: Memory Management, Caching, and Parallelism
9.4 Best Practices for Scaling Spark Applications

10: Deployment and Production Considerations
10.1 Deploying Spark Applications on Different Cluster Managers (YARN, Mesos, Kubernetes)
10.2 Monitoring and Managing Spark Applications in Production
10.3 Handling Data Security and Compliance in Spark Applications
10.4 Best Practices for Maintaining and Scaling Spark Deployments

11: Hands-On Projects and Case Studies
11.1 Real-World Case Studies of Big Data Analytics Using Spark and Java
11.2 Hands-On Project: Building a Complete Big Data Analytics Solution
11.3 Analyzing and Optimizing a Sample Spark Application