1. Introduction to Real-Time Analytics
   1.1 Overview of real-time analytics and its importance
   1.2 Key concepts and challenges in real-time data processing
   1.3 Introduction to Databricks and Spark Streaming
   1.4 Use cases and applications of real-time analytics
2. Getting Started with Spark Streaming in Databricks
   2.1 Overview of Spark Streaming and its architecture
   2.2 Setting up Spark Streaming in a Databricks environment
   2.3 Understanding DStreams (Discretized Streams) and Structured Streaming
   2.4 Basic operations: Creating and managing streaming contexts
3. Data Ingestion for Real-Time Analytics
   3.1 Ingesting data from various sources: Kafka, Azure Event Hubs, AWS Kinesis(Ref: AWS Cloud9 IDE and Java / PHP)
   3.2 Configuring data sources and sinks in Spark Streaming
   3.3 Handling data formats: JSON, Avro, Parquet, etc.
   3.4 Implementing real-time data ingestion pipelines
4. Real-Time Data Processing and Transformation
   4.1 Applying transformations to streaming data: Map, Filter, Reduce, etc.
   4.2 Aggregating and windowing operations in Spark Streaming
   4.3 Joining streaming data with static datasets
   4.4 Handling late-arriving and out-of-order data
5. Fault Tolerance and Scalability in Spark Streaming
   5.1 Understanding Spark Streaming’s fault tolerance mechanisms
   5.2 Implementing checkpointing and recovery strategies
   5.3 Scaling Spark Streaming applications for large data volumes
   5.4 Optimizing performance for high-throughput streaming workloads
6. Integration with Data Storage and Visualization
   6.1 Storing streaming data in real-time databases and data lakes
   6.2 Integrating with data warehouses for real-time analytics
   6.3 Visualizing real-time data using Databricks and BI tools (e.g., Power BI, Tableau)
   6.4 Building dashboards and reports for real-time monitoring
7. Advanced Stream Processing Techniques
   7.1 Implementing advanced analytics with Spark Streaming
   7.2 Using machine learning models for real-time predictions and analysis
   7.3 Integrating with MLflow for real-time model inference
   7.4 Case study: Building an end-to-end real-time analytics application
8. Security and Governance for Real-Time Data
   8.1 Ensuring data security and compliance in streaming applications
   8.2 Implementing access controls and data encryption
   8.3 Managing data privacy and governance in real-time environments
   8.4 Best practices for securing real-time data pipelines
9. Monitoring and Troubleshooting Streaming Applications
   9.1 Monitoring Spark Streaming jobs and performance metrics
   9.2 Diagnosing and troubleshooting common issues in streaming applications
   9.3 Using Databricks tools and logs for troubleshooting
   9.4 Best practices for maintaining streaming application health
10. Best Practices and Real-World Applications
    10.1 Review of best practices for building scalable and efficient streaming applications
    10.2 Case studies of successful real-time analytics implementations
    10.3 Common pitfalls and how to avoid them
    10.4 Future trends and innovations in real-time data processing
11. Final Project: Building a Real-Time Analytics Solution
    11.1 Designing and implementing a real-time analytics solution using Databricks and Spark Streaming
    11.2 Applying learned techniques for data ingestion, processing, and visualization
    11.3 Demonstrating performance optimizations and fault tolerance
    11.4 Presenting and reviewing project outcomes and insights
12. Conclusion and Next Steps
    12.1 Recap of key concepts and techniques covered in the course
    12.2 Additional resources for further learning and certification
    12.3 Career advancement opportunities in real-time data processing and analytics
    12.4 Staying updated with Databricks and Spark Streaming developments.

Reference for Databricks

Reference for Spark Streaming