Part 1. Gentle overview of big data and Spark
1. What is Apache Spark?
2. A gentle introduction to Spark
3. A tour of Spark's toolset

Part 2. Structured APIs : DataFrames, SQL, and datasets
4. Structured API overview
5. Basic structured operations
6. Working with different types of data
7. Aggregations
8. Joins
9. Data sources
10. Spark SQL
11. Datasets

Part 3. Low-level APIs
12. Resilient distributed datasets (RDDs)
13. Advanced RDDs
14. Distributed shared variables

Part 4. Production applications
15. How Spark runs on a cluster
16. Developing Spark applications
17. Deploying Spark
18. Monitoring and debugging
19. Performance tuning

Part 5. Streaming
20. Stream processing fundamentals
21. Structured streaming basics
22. Event-time and stateful processing
23. Structured streaming in production

Part 6. Advanced analytics and machine learning
24. Advanced analytics and machine learning overview
25. Preprocessing and feature engineering
26. Classification
27. Regression
28. Recommendation
29. Unsupervised learning
30. Graph analytics
31. Deep learning

Part 7. Ecosystem
32. Language specifics : Python (PySpark) and R (SparkR and sparklyr)
33. Ecosystem and community

Index