1: Introduction to Data Pipelines
1.1 What are data pipelines?(Ref: ServiceNow DevOps Integration: Streamlining Development Pipelines)
1.2 Batch processing vs. real-time processing
1.3 The role of Apache Spark in building data pipelines

2: Overview of Apache Spark for Data Pipelines
2.1 Spark architecture and components
2.2 Data sources: Structured, unstructured, and streaming data
2.3 Key Spark APIs for building data pipelines (RDDs, DataFrames, Datasets)

3: Setting Up the Development Environment
3.1 Setting up Apache Spark and Java environment
3.2 Understanding dependencies and configurations for building Spark applications
3.3 Running Spark jobs locally and on a cluster

4: Data Ingestion and Sources
4.1 Connecting to external data sources: HDFS, AWS S3, Kafka, and databases
4.2 Handling structured and unstructured data
4.3 Real-time data ingestion with Kafka and Spark Streaming
4.4 Batch data ingestion and ETL processes

5: Building Batch Data Pipelines
5.1 Creating ETL pipelines for batch processing
5.2 Transforming and processing large datasets with RDDs and DataFrames
5.3 Data aggregation, filtering, and sorting operations
5.4 Saving processed data to databases, file systems, and other storage options

6: Real-Time Data Processing with Spark Streaming
6.1 Introduction to Spark Streaming
6.2 Handling streaming data: Sources, sinks, and windowing
6.3 Building real-time pipelines with Spark Streaming and Kafka
6.4 Processing data in micro-batches vs. continuous streaming

7: Data Transformation and Enrichment
7.1 Applying transformation operations on streaming and batch data
7.2 Enriching data: Joining with external datasets
7.3 Using SparkSQL for querying and manipulating structured data
7.4 Optimizing data transformation for performance and scalability

8: Handling Complex Data Workflows
8.1 Managing dependencies and orchestration in data pipelines
8.2 Implementing complex workflows with multi-step pipelines
8.3 Handling fault tolerance and data recovery in Spark

9: Integrating Machine Learning into Data Pipelines
9.1 Introduction to Spark MLlib
9.2 Integrating machine learning models into pipelines
9.3 Building pipelines for training and deploying machine learning models
9.4 Case study: Implementing predictive analytics in a pipeline

10: Data Pipeline Monitoring and Management
10.1 Monitoring Spark jobs and applications
10.2 Logging and debugging Spark pipelines
10.3 Tools for monitoring and managing Spark clusters (Spark UI, Ganglia, etc.)
10.4 Best practices for pipeline performance tuning

11: Deploying and Scaling Data Pipelines
11.1 Deploying pipelines on Spark clusters (YARN, Mesos, Kubernetes)
11.2 Scaling pipelines for large-scale data processing
11.3 Running and scheduling Spark jobs using tools like Apache Airflow
11.4 Case study: Deploying a production-ready data pipeline

12: Hands-On Projects and Case Studies
12.1 Real-world case studies of data pipelines built using Spark and Java
12.2 Hands-on project: Building a complete batch and real-time data pipeline with Spark
12.3 Troubleshooting common challenges in data pipeline development