1. Introduction

1.1 Overview of big data technologies and architectures
1.2 Understanding Talend’s role in big data integration and processing
1.3 Key features and components
1.4 Comparing Talend with other big data integration tools

2. Setting Up

2.1 Configuring its environments
2.2 Integrating Talend with Hadoop and Spark
2.3 Setting up its Platform
2.4 Understanding Talend’s big data components (e.g., tHDFSInput, tSparkJob)

3. Working with Hadoop and Spark

3.1 Overview of Hadoop and Spark ecosystems
3.2 Integrating Talend with Hadoop Distributed File System (HDFS)
3.3 Using Talend to process data with Apache Spark
3.4 Leveraging Spark SQL and Spark Streaming in Talend(Ref: Talend for Cloud Data Integration: Managing Data in the Cloud)

4. Data Integration and Processing Techniques

4.1 Designing ETL workflows
4.2 Using Talend components for data extraction from big data sources
4.3 Transforming and processing large datasets efficiently
4.4 Loading data into big data storage systems

5. Optimizing Performance in Big Data Workflows

5.1 Techniques for optimizing data
5.2 Managing resources and performance tuning
5.3 Implementing parallel processing and distributed computing
5.4 Best practices for handling large-scale data volumes

6. Advanced Data Processing and Transformation

6.1 Implementing advanced data transformations with Talend
6.2 Using Talend’s big data components for complex data processing
6.3 Handling real-time and batch data processing
6.4 Managing data quality and data governance in big data environments

7. Integrating with Big Data Ecosystems

7.1 Connecting Talend with NoSQL databases (e.g., MongoDB, Cassandra)
7.2 Integrating with cloud-based big data services (e.g., AWS EMR, Google BigQuery)
7.3 Leveraging Talend’s connectors and components for various big data tools
7.4 Implementing data synchronization and integration patterns

8. Monitoring and Troubleshooting Big Data Jobs

8.1 Monitoring Talend job execution and performance
8.2 Analyzing and troubleshooting issues in big data workflows
8.3 Using Talend’s logging and error handling features
8.4 Best practices for maintaining and managing big data integrations

9. Case Studies and Real-World Applications

9.1 Analyzing case studies of big data projects using Talend
9.2 Lessons learned from real-world implementations
9.3 Innovative approaches and best practices in big data integration
9.4 Future trends and developments in big data technologies

10. Final Project: Building a Big Data Integration Solution

10.1 Designing and implementing a comprehensive big data integration solution using Talend
10.2 Integrating and processing large datasets with Hadoop and Spark
10.3 Demonstrating performance optimization and advanced processing techniques
10.4 Presenting and reviewing project outcomes and solutions

11. Conclusion and Next Steps

11.1 Recap of key concepts and techniques covered in the course
11.2 Additional resources for further learning and certification
11.3 Career opportunities and advancement in big data and Talend
11.4 Staying updated with big data trends and Talend innovations